《OpenCV3编程入门》第五章core组件进阶学习笔记part2

6.图像对比度，亮度调整

理论公式：g(i,j) = a*f(i,j) + b;

#include <opencv2/core/core.hpp>#include <opencv2/highgui/highgui.hpp>#include "opencv2/imgproc/imgproc.hpp"#include <iostream>//-----------------------------------【命名空间声明部分】---------------------------------------//描述：包含程序所使用的命名空间//-----------------------------------------------------------------------------------------------   using namespace std;using namespace cv;//-----------------------------------【全局函数声明部分】--------------------------------------//描述：全局函数声明//-----------------------------------------------------------------------------------------------static void ContrastAndBright(int, void *);//-----------------------------------【全局变量声明部分】--------------------------------------//描述：全局变量声明//-----------------------------------------------------------------------------------------------int g_nContrastValue; //对比度值int g_nBrightValue;  //亮度值Mat g_srcImage,g_dstImage;//-----------------------------------【main( )函数】--------------------------------------------//描述：控制台应用程序的入口函数，我们的程序从这里开始//-----------------------------------------------------------------------------------------------int main(   ){//改变控制台前景色和背景色system("color 2F");  ShowHelpText();// 读入用户提供的图像g_srcImage = imread( "1.jpg");if( !g_srcImage.data ) { printf("读取g_srcImage图片错误~！ \n"); return false; }g_dstImage = Mat::zeros( g_srcImage.size(), g_srcImage.type() );//设定对比度和亮度的初值g_nContrastValue=80;g_nBrightValue=80;//创建窗口namedWindow("【效果图窗口】", 1);//创建轨迹条createTrackbar("对比度：", "【效果图窗口】",&g_nContrastValue, 300,ContrastAndBright );createTrackbar("亮   度：", "【效果图窗口】",&g_nBrightValue, 200,ContrastAndBright );//调用回调函数ContrastAndBright(g_nContrastValue,0);ContrastAndBright(g_nBrightValue,0);//输出一些帮助信息cout<<endl<<"\t运行成功，请调整滚动条观察图像效果\n\n"<<"\t按下“q”键时，程序退出\n";//按下“q”键时，程序退出while(char(waitKey(1)) != 'q') {}return 0;}//-----------------------------【ContrastAndBright( )函数】------------------------------------//描述：改变图像对比度和亮度值的回调函数//-----------------------------------------------------------------------------------------------static void ContrastAndBright(int, void *){// 创建窗口namedWindow("【原始图窗口】", 1);// 三个for循环，执行运算 g_dstImage(i,j) = a*g_srcImage(i,j) + bfor( int y = 0; y < g_srcImage.rows; y++ ){for( int x = 0; x < g_srcImage.cols; x++ ){for( int c = 0; c < 3; c++ ){g_dstImage.at<Vec3b>(y,x)[c] = saturate_cast<uchar>( (g_nContrastValue*0.01)*( g_srcImage.at<Vec3b>(y,x)[c] ) + g_nBrightValue );}}}// 显示图像imshow("【原始图窗口】", g_srcImage);imshow("【效果图窗口】", g_dstImage);imwrite("效果图.jpg",g_dstImage);}

7.离散傅里叶变换

暂时理解不了理论部分，先把代码贴上来，以后有用到再好好研究

//---------------------------------【头文件、命名空间包含部分】-----------------------------//描述：包含程序所使用的头文件和命名空间//-------------------------------------------------------------------------------------------------#include "opencv2/core/core.hpp"#include "opencv2/imgproc/imgproc.hpp"#include "opencv2/highgui/highgui.hpp"#include <iostream>using namespace cv;//--------------------------------------【main( )函数】-----------------------------------------//          描述：控制台应用程序的入口函数，我们的程序从这里开始执行//-------------------------------------------------------------------------------------------------int main(){//【1】以灰度模式读取原始图像并显示Mat srcImage = imread("1.jpg", 0);if (!srcImage.data) { printf("读取图片错误，请确定目录下是否有imread函数指定图片存在~！ \n"); return false; }imshow("原始图像", srcImage);//【2】将输入图像延扩到最佳的尺寸，边界用0补充int m = getOptimalDFTSize(srcImage.rows);int n = getOptimalDFTSize(srcImage.cols);//将添加的像素初始化为0.Mat padded;copyMakeBorder(srcImage, padded, 0, m - srcImage.rows, 0, n - srcImage.cols, BORDER_CONSTANT, Scalar::all(0));//【3】为傅立叶变换的结果(实部和虚部)分配存储空间。//将planes数组组合合并成一个多通道的数组complexIMat planes[] = { Mat_<float>(padded), Mat::zeros(padded.size(), CV_32F) };Mat complexI;merge(planes, 2, complexI);//【4】进行就地离散傅里叶变换dft(complexI, complexI);//【5】将复数转换为幅值，即=> log(1 + sqrt(Re(DFT(I))^2 + Im(DFT(I))^2))split(complexI, planes); // 将多通道数组complexI分离成几个单通道数组，planes[0] = Re(DFT(I), planes[1] = Im(DFT(I))magnitude(planes[0], planes[1], planes[0]);// planes[0] = magnitude  Mat magnitudeImage = planes[0];//【6】进行对数尺度(logarithmic scale)缩放magnitudeImage += Scalar::all(1);log(magnitudeImage, magnitudeImage);//求自然对数//【7】剪切和重分布幅度图象限//若有奇数行或奇数列，进行频谱裁剪      magnitudeImage = magnitudeImage(Rect(0, 0, magnitudeImage.cols & -2, magnitudeImage.rows & -2));//重新排列傅立叶图像中的象限，使得原点位于图像中心  int cx = magnitudeImage.cols / 2;int cy = magnitudeImage.rows / 2;Mat q0(magnitudeImage, Rect(0, 0, cx, cy));   // ROI区域的左上Mat q1(magnitudeImage, Rect(cx, 0, cx, cy));  // ROI区域的右上Mat q2(magnitudeImage, Rect(0, cy, cx, cy));  // ROI区域的左下Mat q3(magnitudeImage, Rect(cx, cy, cx, cy)); // ROI区域的右下//交换象限（左上与右下进行交换）Mat tmp;q0.copyTo(tmp);q3.copyTo(q0);tmp.copyTo(q3);//交换象限（右上与左下进行交换）q1.copyTo(tmp);q2.copyTo(q1);tmp.copyTo(q2);//【8】归一化，用0到1之间的浮点值将矩阵变换为可视的图像格式//此句代码的OpenCV2版为：//normalize(magnitudeImage, magnitudeImage, 0, 1, CV_MINMAX); //此句代码的OpenCV3版为:normalize(magnitudeImage, magnitudeImage, 0, 1, NORM_MINMAX);//【9】显示效果图imshow("频谱幅值", magnitudeImage);imwrite("效果图.jpg", magnitudeImage);waitKey();return 0;}

原图

效果图

8.XML与YAML文件的写入和读取

//XML与YAML文件的写入#include<opencv2/opencv.hpp>#include<time.h>using namespace cv;int main(){//初始化FileStorage fs("test.yaml", FileStorage::WRITE);//开始文件写入fs << "frameCount" << 5;time_t rawtime; time(&rawtime);fs << "calibrationDate" << asctime(localtime(&rawtime));Mat cameraMatrix = (Mat_<double>(3, 3) << 1000, 0, 320, 0, 1000, 240, 0, 0, 1);Mat distCoeffs = (Mat_<double>(5, 1) << 0.1, 0.01, -0.001, 0, 0);fs << "cameraMatrix" << cameraMatrix << "distCoeffs" << distCoeffs;fs << "features" << "[";for (int i = 0; i < 3; i++){int x = rand() % 640;int y = rand() % 480;uchar lbp = rand() % 256;fs << "{:" << "x" << x << "y" << y << "lbp" << "[:";for (int j = 0; j < 8; j++){fs << ((lbp >> j) & 1);}fs << "]" << "}";}fs << "]";fs.release();printf("文件读写完毕，请在工程目录下查看生成的文件~");getchar();return 0;}#include "opencv2/opencv.hpp"  #include <time.h>  using namespace cv;using namespace std;//XML与YAML文件的读取int main(){//改变console字体颜色system("color 6F");//初始化FileStorage fs2("test.yaml", FileStorage::READ);// 第一种方法，对FileNode操作int frameCount = (int)fs2["frameCount"];std::string date;// 第二种方法，使用FileNode运算符> > fs2["calibrationDate"] >> date;Mat cameraMatrix2, distCoeffs2;fs2["cameraMatrix"] >> cameraMatrix2;fs2["distCoeffs"] >> distCoeffs2;cout << "frameCount: " << frameCount << endl<< "calibration date: " << date << endl<< "camera matrix: " << cameraMatrix2 << endl<< "distortion coeffs: " << distCoeffs2 << endl;FileNode features = fs2["features"];FileNodeIterator it = features.begin(), it_end = features.end();int idx = 0;std::vector<uchar> lbpval;//使用FileNodeIterator遍历序列for (; it != it_end; ++it, idx++){cout << "feature #" << idx << ": ";cout << "x=" << (int)(*it)["x"] << ", y=" << (int)(*it)["y"] << ", lbp: (";// 我们也可以使用使用filenode > > std::vector操作符很容易的读数值阵列(*it)["lbp"] >> lbpval;for (int i = 0; i < (int)lbpval.size(); i++)cout << " " << (int)lbpval[i];cout << ")" << endl;}fs2.release();//程序结束，输出一些帮助文字printf("\n文件读取完毕，请输入任意键结束程序~");getchar();return 0;}

用记事本打开写入的YAML文件