#include"iostream"#include"opencv2/opencv.hpp"#include"iomanip"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); //計算SAD
private:  int winSize; //卷積核的尺寸  int DSR;     //視差搜索范圍};
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)); //視差圖
  for (int i = 0; i//左圖從DSR開始遍歷  {    for (int j = 0; j    {      Kernel_L = L(Rect(i, j, winSize, winSize));      Mat MM(1, DSR, CV_32F, Scalar(0));       for (int k = 0; k      {        int x = i - k;        if (x >= 0)        {          Kernel_R = R(Rect(x, j, winSize, winSize));          Mat Dif;          absdiff(Kernel_L, Kernel_R, Dif);//求差的絕對值之和          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; //顯示處理進(jìn)度  }  return Disparity;}
調(diào)用示例：#include"SAD_Algorithm.h"int main(int argc, char* argv[]){  Mat Img_L = imread("Teddy_L.png", 0);   //此處調(diào)用的圖像已放入項目文件夾中  Mat Img_R = imread("Teddy_R.png", 0);  Mat Disparity;                            //創(chuàng)建視差圖
  SAD mySAD(7, 30);                        //給出SAD的參數(shù)
  Disparity = mySAD.computerSAD(Img_L, Img_R);  imshow("Teddy_L", Img_L);  imshow("Teddy_R", Img_R);  imshow("Disparity", Disparity);        //顯示視差圖
  waitKey();  system("pause");                        //按任意鍵退出  return 0;}

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 sgbm = StereoSGBM::create(0, 16, 3);
  int cn = Img_L.channels();                        //左圖像的通道數(shù)  int SADWindowSize = 9;  int sgbmWinSize = SADWindowSize > 0 ? SADWindowSize : 3;
  sgbm->setMinDisparity(0);                         //minDisparity最小視差默認(rèn)為0；
  sgbm->setNumDisparities(numberOfDisparities);     //numDisparity視差搜索范圍，其值必須為16的整數(shù)倍；
  sgbm->setP1(8 * cn*sgbmWinSize*sgbmWinSize);  sgbm->setP2(32 * cn*sgbmWinSize*sgbmWinSize);     //一般建議懲罰系數(shù)P1、P2取此兩值，P1、P2控制視差圖的光滑度                                                    //P2越大，視差圖越平滑
  sgbm->setDisp12MaxDiff(1);                        //左右一致性檢測最大容許誤差閾值
  sgbm->setPreFilterCap(31);                        //預(yù)處理濾波器的截斷值，預(yù)處理的輸出值僅保留                                                    //[-preFilterCap, preFilterCap]范圍內(nèi)的值，參數(shù)范圍：1 - 31
  sgbm->setUniquenessRatio(10);                     //視差唯一性百分比：視差窗口范圍內(nèi)最低代價是次低代價的(1 + uniquenessRatio/100)倍時                                                    //最低代價對應(yīng)的視差值才是該像素點(diǎn)的視差，否則該像素點(diǎn)的視差為 0 ，不能為負(fù)值，一般去5——15
  sgbm->setSpeckleWindowSize(100);                  //視差連通區(qū)域像素點(diǎn)個數(shù)的大小：對于每一個視差點(diǎn)，當(dāng)其連通區(qū)域的像素點(diǎn)個數(shù)小于                                                    //speckleWindowSize時，認(rèn)為該視差值無效，是噪點(diǎn)。
  sgbm->setSpeckleRange(32);                        //視差連通條件：在計算一個視差點(diǎn)的連通區(qū)域時，當(dāng)下一個像素點(diǎn)視差變化絕對值大于                                                    //speckleRange就認(rèn)為下一個視差像素點(diǎn)和當(dāng)前視差像素點(diǎn)是不連通的。
  sgbm->setMode(0);                                 //模式選擇
  sgbm->setBlockSize(sgbmWinSize);                  //設(shè)置SAD代價計算窗口，一般在3*3到21*21之間                                                    //blockSize(SADWindowSize) 越小，也就是匹配代價計算的窗口越小，視差圖噪聲越大；                                                    //blockSize越大，視差圖越平滑；                                                    //太大的size容易導(dǎo)致過平滑，并且誤匹配增多，體現(xiàn)在視差圖中空洞增多
//三種模式選擇（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位有符號轉(zhuǎn)為8位無符號  imgDisparity16S.convertTo(imgDisparity8U, CV_8U, 255 / (numberOfDisparities*16.));}
調(diào)用示例：#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);//創(chuàng)建一個Disparity圖像
  calDispWithSGBM(Img_L, Img_R, Disparity8U);  imshow("Teddy_L", Img_L);  imshow("Teddy_R", Img_R);  imshow("Disparity", Disparity8U);
  waitKey();  system("pause");  //按任意鍵退出  return 0;}