上一篇介绍了双目立体匹配SAD算法，这一篇介绍Census算法。
Census原理：
在视图中选取任一点，以该点为中心划出一个例如3 × 3 的矩形，矩形中除中心点之外的每一点都与中心点进行比较，灰度值小于中心点记为1，灰度大于中心点的则记为0，以所得长度为 8 的只有 0 和 1 的序列作为该中心点的 census 序列,即中心像素的灰度值被census 序列替换。经过census变换后的图像使用汉明距离计算相似度，所谓图像匹配就是在匹配图像中找出与参考像素点相似度最高的点，而汉明距正是匹配图像像素与参考像素相似度的度量。具体而言，对于欲求取视差的左右视图，要比较两个视图中两点的相似度，可将此两点的census值逐位进行异或运算，然后计算结果为1 的个数，记为此两点之间的汉明值，汉明值是两点间相似度的一种体现，汉明值愈小，两点相似度愈大实现算法时先异或再统计1的个数即可，汉明距越小即相似度越高。
下面的代码是自己根据原理写的，实现的结果并没有很好，以后继续优化代码。
具体代码如下：
//*************************Census*********************
#include <iostream>
#include <opencv2/opencv.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
using namespace std;
using namespace cv;
//-------------------定义汉明距离----------------------------
int disparity;
int GetHammingWeight(uchar value);//求1的个数
//-------------------定义Census处理图像函数---------------------
int hWind = 1;//定义窗口大小为（2*hWind+1）
Mat ProcessImg(Mat &Img);//将矩形内的像素与中心像素相比较，将结果存于中心像素中
Mat Img_census, Left_census, Right_census;
//--------------------得到Disparity图像------------------------
Mat getDisparity(Mat &left, Mat &right);
//--------------------处理Disparity图像-----------------------
Mat ProcessDisparity(Mat &disImg);
int ImgHeight, ImgWidth;
//int num = 0;//异或得到的海明距离
Mat LeftImg, RightImg;
Mat DisparityImg(ImgHeight, ImgWidth, CV_8UC1, Scalar::all(0));
Mat DisparityImg_Processed(ImgHeight, ImgWidth, CV_8UC1, Scalar::all(0));
Mat DisparityImg_Processed_2(ImgHeight, ImgWidth, CV_8UC1);
//定义读取图片的路径
string file_dir="C:\\Program Files\\FLIR Integrated Imaging Solutions\\Triclops Stereo Vision SDK\\stereomatching\\Grab_Stereo\\pictures\\";
//定义存储图片的路径
string save_dir= "C:\\Program Files\\FLIR Integrated Imaging Solutions\\Triclops Stereo Vision SDK\\stereomatching\\Grab_Stereo\\Census\\";
int main()
{
LeftImg = imread(file_dir + "renwu_left.webp", 0);
RightImg = imread(file_dir + "renwu_right.webp", 0);
namedWindow("renwu_left", 1);
namedWindow("renwu_right", 1);
imshow("renwu_left", LeftImg);
waitKey(5);
imshow("renwu_right", RightImg);
waitKey(5);
ImgHeight = LeftImg.rows;
ImgWidth = LeftImg.cols;
Left_census= ProcessImg(LeftImg);//处理左图，得到左图的CENSUS图像 Left_census
namedWindow("Left_census", 1);
imshow("Left_census", Left_census);
waitKey(5);
// imwrite(save_dir + "renwu_left.webp", Left_census);
Right_census= ProcessImg(RightImg);
namedWindow("Right_census", 1);
imshow("Right_census", Right_census);
waitKey(5);
// imwrite(save_dir + "renwu_right.webp", Right_census);
DisparityImg= getDisparity(Left_census, Right_census);
namedWindow("Disparity", 1);
imshow("Disparity", DisparityImg);
// imwrite(save_dir + "disparity.webp", DisparityImg);
waitKey(5);
DisparityImg_Processed = ProcessDisparity(DisparityImg);
namedWindow("DisparityImg_Processed", 1);
imshow("DisparityImg_Processed", DisparityImg_Processed);
// imwrite(save_dir + "disparity_processed.webp", DisparityImg_Processed);
waitKey(0);
return 0;
}
//-----------------------对图像进行census编码---------------
Mat ProcessImg(Mat &Img)
{
int64 start, end;
start = getTickCount();
Mat Img_census = Mat(Img.rows, Img.cols, CV_8UC1, Scalar::all(0));
uchar center = 0;
for (int i = 0; i < ImgHeight - hWind; i++)
{
for (int j = 0; j < ImgWidth - hWind; j++)
{
center = Img.at<uchar>(i + hWind, j + hWind);
uchar census = 0;
uchar neighbor = 0;
for (int p = i; p <= i + 2 * hWind; p++)//行
{
for (int q = j; q <= j + 2 * hWind; q++)//列
{
if (p >= 0 && p <ImgHeight && q >= 0 && q < ImgWidth)
{
if (!(p == i + hWind && q == j + hWind))
{
//--------- 将二进制数存在变量中-----
neighbor = Img.at<uchar>(p, q);
if (neighbor > center)
{
census = census * 2;//向左移一位，相当于在二进制后面增添0
}
else
{
census = census * 2 + 1;//向左移一位并加一，相当于在二进制后面增添1
}
//cout << "census = " << static_cast<int>(census) << endl;
}
}
}
}
Img_census.at<uchar>(i + hWind, j + hWind) = census;
}
}
/*end = getTickCount();
cout << "time is = " << end - start << " ms" << endl;*/
return Img_census;
}
//------------得到汉明距离---------------
int GetHammingWeight( uchar value)
{
int num = 0;
if (value == 0)
return 0;
while (value)
{
++num;
value = (value - 1)&value;
}
return num;
}
//--------------------得到视差图像--------------
Mat getDisparity(Mat &left, Mat &right)
{
int DSR =16;//视差搜索范围
Mat disparity(ImgHeight,ImgWidth,CV_8UC1);
cout << "ImgHeight = " << ImgHeight << " " << "ImgWidth = " << ImgWidth << endl;
for (int i = 0; i < ImgHeight; i++)
{
for (int j = 0; j < ImgWidth; j++)
{
uchar L;
uchar R;
uchar diff;
L = left.at<uchar>(i, j);
Mat Dif(1, DSR, CV_8UC1);
// Mat Dif(1, DSR, CV_32F);
for (int k = 0; k < DSR; k++)
{
//cout << "k = " << k << endl;
int y = j - k;
if (y < 0)
{
Dif.at<uchar>(k) = 0;
}
if (y >= 0)
{
R = right.at<uchar>(i,y);
//bitwise_xor(L, R, );
diff = L^R;
diff = GetHammingWeight(diff);
Dif.at<uchar>(k) = diff;
// Dif.at<float>(k) = diff;
}
}
//---------------寻找最佳匹配点--------------
Point minLoc;
minMaxLoc(Dif, NULL, NULL, &minLoc, NULL);
int loc = minLoc.x;
//cout << "loc..... = " << loc << endl;
disparity.at<uchar>(i,j)=loc*16;
}
}
return disparity;
}
//-------------对得到的视差图进行处理-------------------
Mat ProcessDisparity(Mat &disImg)
{
Mat ProcessDisImg(ImgHeight,ImgWidth,CV_8UC1);//存储处理后视差图
for (int i = 0; i < ImgHeight; i++)
{
for (int j = 0; j < ImgWidth; j++)
{
uchar pixel = disImg.at<uchar>(i, j);
if (pixel < 100)
pixel = 0;
ProcessDisImg.at<uchar>(i, j) = pixel;
}
}
return ProcessDisImg;
}
经过处理后的左图census图像
经过处理后的右图census图像
disparity图像
处理后的disparity图像
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