图像数据归一化

图像数据归一化

Matlab编程：

depth为源图像，数据分布在[0 63],归一化到[0 255]

ymin = 0;

ymax = 255;

depth = getnormaldata(depth, ymin, ymax)

 

function dst = getnormaldata(src, ymin, ymax)
src = double(src);
[m n] = size(src);
xmin = min(min(src));
xmax = max(max(src));
dst = (ymax-ymin)*(src-ones(m,n) * xmin)/(xmax-xmin) + ones(m,n)*ymin;

 

 

opencv编程：

 

normalize与convertTo

cv::Mat mat1=Mat(2,2,CV_32FC1);
mat1.at<float>(0,0) = 1.0f;
mat1.at<float>(0,1) = 2.0f;
mat1.at<float>(1,0) = 3.0f;
mat1.at<float>(1,1) = 4.0f;
// 对于这种小矩阵，还有更简单的赋值方式，找时间再改
cout<<"Mat 1:"<<endl;
cout<<mat1<<endl;

normalize(mat1,mat1,1.0,0.0,NORM_MINMAX);
cout<<"Mat 2:"<<endl;
cout<<mat1<<endl;

 

 double costmax, costmin;
 cv::minMaxIdx(ImgCost, &costmin,&costmax);
 Mat cost(height, width, CV_8UC1);
 //normalize(ImgCost, cost, 255,0,NORM_MINMAX);
 ImgCost.convertTo(cost, CV_8U, 255/costmax);