SAD, SGBM算法代码

1. SAD

SAD_Algorithm.h

#pragma once
#include 
#include 
#include 
using namespace std;
using namespace cv;

class SAD
{
public:
    SAD() :winSize(7), DSR(30) {}
    SAD(int _winSize, int _DSR) :winSize(_winSize), DSR(_DSR) {}
    Mat computerSAD(Mat& L, Mat& R); //计算SAD

private:
    int winSize; //卷积核的尺寸
    int DSR;     //视差搜索范围
};


Mat SAD::computerSAD(Mat& L, Mat& R)
{
    int Height = L.rows;
    int Width = L.cols;


    Mat Kernel_L(Size(winSize, winSize), CV_8U, Scalar::all(0));
    Mat Kernel_R(Size(winSize, winSize), CV_8U, Scalar::all(0));
    Mat Disparity(Height, Width, CV_8U, Scalar(0)); //视差图


    for (int i = 0; i < Width - winSize; i++)  //左图从DSR开始遍历
    {
        for (int j = 0; j < Height - winSize; j++)
        {
            Kernel_L = L(Rect(i, j, winSize, winSize));
            Mat MM(1, DSR, CV_32F, Scalar(0));
            for (int k = 0; k < DSR; k++)
            {
                int x = i - k;
                if (x >= 0)
                {
                    Kernel_R = R(Rect(x, j, winSize, winSize));
                    Mat Dif;
                    absdiff(Kernel_L, Kernel_R, Dif);//求差的绝对值之和
                    Scalar ADD = sum(Dif);
                    float a = ADD[0];
                    MM.at<float>(k) = a;
                }
            }
            Point minLoc;
            minMaxLoc(MM, NULL, NULL, &minLoc, NULL);
            int loc = minLoc.x;
            //int loc=DSR-loc;
            Disparity.at<char>(j, i) = loc * 16;
        }
        double rate = double(i) / (Width);
        cout << "已完成" << setprecision(2) << rate * 100 << "%" << endl; //显示处理进度
    }
    return Disparity;
}

SAD_Algorithm.cpp

#include"SAD_Algorithm.h"
int main(int argc, char* argv[])
{
    Mat Img_L = imread("Teddy_L.png", 0);   //此处调用的图像已放入项目文件夹中
    Mat Img_R = imread("Teddy_R.png", 0);
    Mat Disparity;                            //创建视差图

    SAD mySAD(7, 30);                        //给出SAD的参数


    Disparity = mySAD.computerSAD(Img_L, Img_R);
    imshow("Teddy_L", Img_L);
    imshow("Teddy_R", Img_R);
    imshow("Disparity", Disparity);        //显示视差图

    waitKey();
    system("pause");                        //按任意键退出
    return 0;
}

2. SGBM

SGBM_Algorithm.h

#pragma once
enum { STEREO_BM = 0, STEREO_SGBM = 1, STEREO_HH = 2, STEREO_VAR = 3, STEREO_3WAY = 4 };
#include"iostream"
#include"opencv2/opencv.hpp"
using namespace std;
using namespace cv;


void calDispWithSGBM(Mat Img_L, Mat Img_R, Mat& imgDisparity8U)
{
    Size imgSize = Img_L.size();
    int numberOfDisparities = ((imgSize.width / 8) + 15) & -16;
    Ptr<StereoSGBM> sgbm = StereoSGBM::create(0, 16, 3);


    int cn = Img_L.channels();                        //左图像的通道数
    int SADWindowSize = 9;
    int sgbmWinSize = SADWindowSize > 0 ? SADWindowSize : 3;


    sgbm->setMinDisparity(0);                         //minDisparity最小视差默认为0；


    sgbm->setNumDisparities(numberOfDisparities);     //numDisparity视差搜索范围，其值必须为16的整数倍；


    sgbm->setP1(8 * cn * sgbmWinSize * sgbmWinSize);
    sgbm->setP2(32 * cn * sgbmWinSize * sgbmWinSize);     //一般建议惩罚系数P1、P2取此两值，P1、P2控制视差图的光滑度
                                                      //P2越大，视差图越平滑


    sgbm->setDisp12MaxDiff(1);                        //左右一致性检测最大容许误差阈值


    sgbm->setPreFilterCap(31);                        //预处理滤波器的截断值，预处理的输出值仅保留
                                                      //[-preFilterCap, preFilterCap]范围内的值，参数范围：1 - 31


    sgbm->setUniquenessRatio(10);                     //视差唯一性百分比：视差窗口范围内最低代价是次低代价的(1 + uniquenessRatio/100)倍时
                                                      //最低代价对应的视差值才是该像素点的视差，否则该像素点的视差为 0 ，不能为负值，一般去5——15


    sgbm->setSpeckleWindowSize(100);                  //视差连通区域像素点个数的大小：对于每一个视差点，当其连通区域的像素点个数小于
                                                      //speckleWindowSize时，认为该视差值无效，是噪点。


    sgbm->setSpeckleRange(32);                        //视差连通条件：在计算一个视差点的连通区域时，当下一个像素点视差变化绝对值大于
                                                      //speckleRange就认为下一个视差像素点和当前视差像素点是不连通的。


    sgbm->setMode(0);                                 //模式选择


    sgbm->setBlockSize(sgbmWinSize);                  //设置SAD代价计算窗口，一般在3*3到21*21之间
                                                      //blockSize(SADWindowSize) 越小，也就是匹配代价计算的窗口越小，视差图噪声越大；
                                                      //blockSize越大，视差图越平滑；
                                                      //太大的size容易导致过平滑，并且误匹配增多，体现在视差图中空洞增多


  //三种模式选择（HH、SGBM、3WAY）
    int algorithm = STEREO_SGBM;


    if (algorithm == STEREO_HH)
        sgbm->setMode(StereoSGBM::MODE_HH);
    else if (algorithm == STEREO_SGBM)
        sgbm->setMode(StereoSGBM::MODE_SGBM);
    else if (algorithm == STEREO_3WAY)
        sgbm->setMode(StereoSGBM::MODE_SGBM_3WAY);


    Mat imgDisparity16S = Mat(Img_L.rows, Img_L.cols, CV_16S);


    sgbm->compute(Img_L, Img_R, imgDisparity16S);


    //--Display it as a CV_8UC1 image：16位有符号转为8位无符号
    imgDisparity16S.convertTo(imgDisparity8U, CV_8U, 255 / (numberOfDisparities * 16.));
}

SGBM_Algorithm.cpp

#include"SGBM_Algorithm.h"
int main()
{
	Mat Img_L = imread("Teddy_L.png", 0);
	Mat Img_R = imread("Teddy_R.png", 0);
	Mat Disparity8U = Mat(Img_L.rows, Img_R.cols, CV_8UC1);//创建一个Disparity图像


	calDispWithSGBM(Img_L, Img_R, Disparity8U);
	imshow("Teddy_L", Img_L);
	imshow("Teddy_R", Img_R);
	imshow("Disparity", Disparity8U);


	waitKey();
	system("pause");  //按任意键退出
	return 0;

}