1、积分图像

#include <iostream>
#include <opencv2/opencv.hpp>using namespace cv;
using namespace std;//积分图像
int test()
{//创建一个16×16全为1的矩阵,因为256=16×16Mat img = Mat::ones(16, 16, CV_32FC1);//在图像中加入随机噪声RNG rng(10086);for (int y = 0; y < img.rows; y++){for (int x = 0; x < img.cols; x++){float d = rng.uniform(-0.5, 0.5);//使用.at的形式读出x，y位置像素来进行操作img.at<float>(y, x) = img.at<float>(y, x) + d;}}//计算标准求和积分Mat sum;integral(img, sum);//为了便于显示,转成CV_8U格式Mat sum8U = Mat_<uchar>(sum);namedWindow("sum8U", WINDOW_NORMAL);imshow("sum8U", sum8U);//计算平方求和积分Mat sqsum;integral(img, sum, sqsum);//为了便于显示,转成CV_8U格式Mat sqsum8U = Mat_<uchar>(sqsum);namedWindow("sqsum8U", WINDOW_NORMAL);imshow("sqsum8U", sqsum8U);//计算倾斜求和积分Mat tilted;integral(img, sum, sqsum, tilted);//为了便于显示,转成CV_8U格式Mat tilted8U = Mat_<uchar>(tilted);namedWindow("tilted8U", WINDOW_NORMAL);imshow("tilted8U", tilted8U);waitKey(0);return 0;
}
int main()
{test();system("pause");return 0;
}

2、图像分割--漫水填充

#include <iostream>
#include <opencv2/opencv.hpp>using namespace cv;
using namespace std;//图像分割--漫水填充
int test()
{system("color 02");Mat img = imread("E:/testMap/lena.png");if (!(img.data)){cout << "读取图像错误,请确认图像文件是否正确" << endl;return -1;}RNG rng(10086);//随机数,用于随机生成像素//设置操作标志flagsint connectivity = 4;//连通邻域方式int maskVal = 255;//掩码图像的数值int flags = connectivity | (maskVal << 8) | FLOODFILL_FIXED_RANGE;//漫水填充操作方式标志//设置与选中像素点的差值Scalar loDiff = Scalar(20, 20, 20);Scalar upDiff = Scalar(20, 20, 20);//声明掩模矩阵变量，尺寸比输入图像宽高各大2Mat mask = Mat::zeros(img.rows + 2, img.cols + 2, CV_8UC1);while (true){//随机产生图像中某一像素点int py = rng.uniform(0, img.rows - 1);int px = rng.uniform(0, img.cols - 1);Point point = Point(px, py);//彩色图像中填充的像素值Scalar newVal = Scalar(rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255));//漫水填充函数int area = floodFill(img, mask, point, newVal, &Rect(), loDiff, upDiff, flags);//输出像素点和填充的像素数目cout << "像素点x: " << point.x << " y : " << point.y << "填充像素数目:" << area << endl;//输出填充的图像结果imshow("填充的彩色图像", img);imshow("掩模图像", mask);//判断是否结束程序int c = waitKey();if ((c & 255) == 27){break;}}waitKey(0);return 0;
}
int main()
{test();system("pause");return 0;
}

3、图像分割--分水岭法

#include <opencv2/opencv.hpp>
#include <iostream>using namespace cv;
using namespace std;//图像分割--分水岭法
int test()
{Mat img, imgGray, imgMask, img_;Mat maskWaterShed; //watershed()函数的参数img = imread("E:/testMap/lenaw.png"); //含有标记的图像img_ = imread("E:/testMap/lena.png"); //原图像cvtColor(img, imgGray, COLOR_BGR2GRAY);threshold(imgGray, imgMask, 235, 255, THRESH_BINARY);//二值化Mat k = getStructuringElement(0, Size(3, 3));//生成结构元素morphologyEx(imgMask, imgMask, MORPH_OPEN, k);//开运算imshow("含有标记的图像", img);imshow("原图像", img_);vector<vector<Point>> contours;vector<Vec4i> hierarchy;//轮廓索引编号findContours(imgMask, contours, hierarchy, RETR_CCOMP, CHAIN_APPROX_SIMPLE);//在maskWaterShed上绘制轮廓,用于输入分水岭算法maskWaterShed = Mat::zeros(imgMask.size(), CV_32S);for (int index = 0; index < contours.size(); index++){//其中第一个参数image表示目标图像，第二个参数contours表示输入的轮廓组，每一组轮廓由点vector构成，//第三个参数contourIdx指明画第几个轮廓，如果该参数为负值，则画全部轮廓，第四个参数color为轮廓的颜色，//第五个参数thickness为轮廓的线宽，如果为负值或CV_FILLED表示填充轮廓内部，第六个参数lineType为线型，//第七个参数为轮廓结构信息，	第八个参数为maxLeveldrawContours(maskWaterShed, contours, index, Scalar::all(index + 1), -1, 8, hierarchy, INT_MAX);}//分水岭算法需要对原图像进行处理watershed(img_, maskWaterShed);vector<Vec3b> colors;// 随机生成几种颜色for (int i = 0; i < contours.size(); i++){int b = theRNG().uniform(0, 255);int g = theRNG().uniform(0, 255);int r = theRNG().uniform(0, 255);colors.push_back(Vec3b((uchar)b, (uchar)g, (uchar)r));}Mat resultImg = Mat(img.size(), CV_8UC3); // 显示图像for (int i = 0; i < imgMask.rows; i++){for (int j = 0; j < imgMask.cols; j++){//绘制每个区域的颜色int index = maskWaterShed.at<int>(i, j);if (index == -1)//区域间的值被置为 - 1(边界){resultImg.at<Vec3b>(i, j) = Vec3b(255, 255, 255);}else if (index <= 0 || index > contours.size())//没有标记清楚的区域被置为0{resultImg.at<Vec3b>(i, j) = Vec3b(0, 0, 0);}else//其他每个区域的值保持不变: 1,2,…,contours.size(){resultImg.at<Vec3b>(i, j) = colors[index - 1];//把些区域绘制成不同颜色}}}imshow("resultImg", resultImg);resultImg = resultImg * 0.8 + img_*0.2;//addWeighted(resultImg,0.8,img_, 0.2,0, resultImg); imshow("分水岭结果", resultImg);//绘制每个区域的图像for (int n = 1; n <= contours.size(); n++){Mat resImagel = Mat(img.size(), CV_8UC3);//声明一个最后要显示的图像for (int i = 0; i < imgMask.rows; i++){for (int j = 0; j < imgMask.cols; j++){int index = maskWaterShed.at<int>(i, j);if (index == n)resImagel.at<Vec3b>(i, j) = img_.at<Vec3b>(i, j);elseresImagel.at<Vec3b>(i, j) = Vec3b(0, 0, 0);}}//显示图像imshow(to_string(n), resImagel);}waitKey(0);return 0;
}int main()
{test();system("pause");return 0;
}

4、Harris角点检测

#include <opencv2/opencv.hpp>
#include <iostream>using namespace cv;
using namespace std;//Harris角点检测
int test()
{Mat img = imread("E:/testMap/lena.png", IMREAD_COLOR);if (!img.data){cout << "读取图像错误,请确认图像文件是否正确" << endl;return -1;}//转成灰度图像Mat gray;cvtColor(img, gray, COLOR_BGR2GRAY);//计算Harris系数Mat harris;int blockSize = 2; //邻域半径int apertureSize = 3;cornerHarris(gray, harris, blockSize, apertureSize, 0.04);//角点检测//归一化便于进行数值比较和结果显示Mat harrisn;normalize(harris, harrisn, 0, 255, NORM_MINMAX);//将图像的数据类型变成CV_8UconvertScaleAbs(harrisn, harrisn);//寻找Harris角点vector<KeyPoint> keyPoints;for (int row = 0; row < harrisn.rows; row++){for (int col = 0; col < harrisn.cols; col++){int R = harrisn.at<uchar>(row, col);if (R >125){//向角点存入KeyPoint中KeyPoint keyPoint;keyPoint.pt.y = row;keyPoint.pt.x = col;keyPoints.push_back(keyPoint);}}}//绘制角点与显示结果drawKeypoints(img, keyPoints, img);imshow("系数矩阵", harrisn);imshow("Harris角点", img);waitKey(0);return 0;
}int main()
{test();system("pause");return 0;
}