我正在尝试使用此处提出的代码http://ivrlwww.epfl.ch/supplementary_material/RK_CVPR09/进行彩色图像的显着性检测。提出的代码与在windows中开发的GUI相关联。在我的情况下，我想在Mac OsX上使用OpenCv库来读取初始图像并写出显着性图结果。因此，我选择了四个主要功能，并使用OpenCV修改了读写块。我得到了以下结果，这些结果与作者所获得的结果略有不同：
以下是四个功能。我做错了有什么不对吗？我小心地考虑在OpenCV中，颜色被描述为B-G-R而不是R-G-B。
#include <stdio.h>
#include <opencv2/opencv.hpp>
#include <iostream>
using namespace cv;
using namespace std;
void RGB2LAB2(
const vector<vector<uint> > &ubuff,
vector<double>& lvec,
vector<double>& avec,
vector<double>& bvec){
int sz = int(ubuff.size());
cout<<"sz "<<sz<<endl;
lvec.resize(sz);
avec.resize(sz);
bvec.resize(sz);
for( int j = 0; j < sz; j++ ){
int sR = ubuff[j][2];
int sG = ubuff[j][1];
int sB = ubuff[j][0];
//------------------------
// sRGB to XYZ conversion
// (D65 illuminant assumption)
//------------------------
double R = sR/255.0;
double G = sG/255.0;
double B = sB/255.0;
double r, g, b;
if(R <= 0.04045) r = R/12.92;
else r = pow((R+0.055)/1.055,2.4);
if(G <= 0.04045) g = G/12.92;
else g = pow((G+0.055)/1.055,2.4);
if(B <= 0.04045) b = B/12.92;
else b = pow((B+0.055)/1.055,2.4);
double X = r*0.4124564 + g*0.3575761 + b*0.1804375;
double Y = r*0.2126729 + g*0.7151522 + b*0.0721750;
double Z = r*0.0193339 + g*0.1191920 + b*0.9503041;
//------------------------
// XYZ to LAB conversion
//------------------------
double epsilon = 0.008856; //actual CIE standard
double kappa = 903.3; //actual CIE standard
double Xr = 0.950456; //reference white
double Yr = 1.0; //reference white
double Zr = 1.088754; //reference white
double xr = X/Xr;
double yr = Y/Yr;
double zr = Z/Zr;
double fx, fy, fz;
if(xr > epsilon) fx = pow(xr, 1.0/3.0);
else fx = (kappa*xr + 16.0)/116.0;
if(yr > epsilon) fy = pow(yr, 1.0/3.0);
else fy = (kappa*yr + 16.0)/116.0;
if(zr > epsilon) fz = pow(zr, 1.0/3.0);
else fz = (kappa*zr + 16.0)/116.0;
lvec[j] = 116.0*fy-16.0;
avec[j] = 500.0*(fx-fy);
bvec[j] = 200.0*(fy-fz);
}
}
void 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;
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++;
}
}
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++;
}
}
}
void GetSaliencyMap(
const vector<vector<uint> >&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);
RGB2LAB2(inputimg, lvec, avec, bvec);
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);
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);
}
}
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++;
}
}
}
int main(){
Mat image;
image = imread( argv[1], 1 );
if ( !image.data ){
printf("No image data \n");
return -1;
}
std::vector<vector<uint>>array(image.cols*image.rows,vector<uint>
(3,0));
for(int y=0;y<image.rows;y++){
for(int x=0;x<image.cols;x++){
Vec3b color= image.at<Vec3b>(Point(x,y));
array[image.cols*y+x][0]=color[0]; array[image.cols*y+x]
[1]=color[1];array[image.cols*y+x][2]=color[2];
}
}
vector<double> salmap; bool normflag=true;
GetSaliencyMap(array, image.size().width, image.size().height, salmap,
normflag);
Mat output;
output = Mat( image.rows, image.cols,CV_8UC1);
int k=0;
for(int y=0;y<image.rows;y++){
for(int x=0;x<image.cols;x++){
output.at<uchar>(Point(x,y)) = int(salmap[k]);
k++;
}
}
imwrite("test_saliency_blackAndWhite.jpg", output );
return 0;
}