OpenCV官方文档学习记录（1）

图像显示并转化为黑白输出到新文件

code：

#include <opencv2\opencv.hpp>
#include <iostream>
#include <string>

#pragma comment( linker, "/subsystem:\"windows\" /entry:\"mainCRTStartup\"" )

using namespace std;
using namespace cv;

int main()
{
Mat img = imread("test.jpg",1);
if (img.empty())
{
return -1;
}
Mat gray_img;
cvtColor(img, gray_img, CV_BGR2GRAY);
imwrite("./black.jpg", gray_img);

namedWindow("Source", CV_WINDOW_AUTOSIZE);
imshow("Source", img);
namedWindow("Black", CV_WINDOW_AUTOSIZE);
imshow("Black", gray_img);

waitKey();
return 0;
}

结果：



Mat数据结构：

#include <opencv2\opencv.hpp>
#include <iostream>
#include <string>

#pragma comment( linker, "/subsystem:\"windows\" /entry:\"mainCRTStartup\"" )

using namespace std;
using namespace cv;

void Show(std::string name,Mat img)
{
namedWindow(name, CV_WINDOW_AUTOSIZE);
imshow(name, img);
}

int main()
{
Mat A, C;
A = imread("test.jpg", 1);
Mat B(A);
C = A;
Mat D(A, Rect(10, 10, 100, 100));
Mat E = A(Range::all(), Range(1, 3));
Show("A", A);
Show("B", B);
Show("C", C);
Show("D", D);
Show("E", E);
waitKey();
return 0;
}

结果：



以上显示的是浅拷贝，就是除了headers不同之外，其余的数据都是从一块数据块中取得的。

要实现深拷贝：

#include <opencv2\opencv.hpp>
#include <iostream>
#include <string>

#pragma comment( linker, "/subsystem:\"windows\" /entry:\"mainCRTStartup\"" )

using namespace std;
using namespace cv;

void Show(std::string name,Mat img)
{
namedWindow(name, CV_WINDOW_AUTOSIZE);
imshow(name, img);
}

int main()
{
Mat img = imread("test.jpg", 1);
Show("Source", img);
Mat dest = img.clone();
Show("Dest", dest);
waitKey();
return 0;
}

或是：

#include <opencv2\opencv.hpp>
#include <iostream>
#include <string>

#pragma comment( linker, "/subsystem:\"windows\" /entry:\"mainCRTStartup\"" )

using namespace std;
using namespace cv;

void Show(std::string name,Mat img)
{
namedWindow(name, CV_WINDOW_AUTOSIZE);
imshow(name, img);
}

int main()
{
Mat img = imread("test.jpg", 1);
Show("Source", img);
Mat dest;
img.copyTo(dest);
Show("Dest", dest);
waitKey();
return 0;
}

构建Mat对象：

#include <opencv2\opencv.hpp>
#include <iostream>
#include <string>

//#pragma comment( linker, "/subsystem:\"windows\" /entry:\"mainCRTStartup\"" )

using namespace std;
using namespace cv;

void Show(std::string name,Mat img)
{
namedWindow(name, CV_WINDOW_AUTOSIZE);
imshow(name, img);
}

int main()
{
Mat M(2, 2, CV_8UC3, Scalar(0, 0, 255));
cout << "M=" << endl << "" << M << endl << endl;
system("pause");
return 0;
}

构造器参数，行+列+图像类型+填充数据。

模仿MatLab的函数：

#include <opencv2\opencv.hpp>
#include <iostream>
#include <string>

//#pragma comment( linker, "/subsystem:\"windows\" /entry:\"mainCRTStartup\"" )

using namespace std;
using namespace cv;

void Show(std::string name,Mat img)
{
namedWindow(name, CV_WINDOW_AUTOSIZE);
imshow(name, img);
}

int main()
{
Mat E = Mat::eye(4, 4, CV_64F);
cout << E << endl;
Mat O = Mat::ones(2, 2, CV_32F);
cout << O << endl;
Mat Z = Mat::zeros(3, 3, CV_8UC1);
cout << Z << endl;
system("pause");
return 0;
}

因为默认重载了流运算符，因此可以直接输出；

对于小矩阵使用逗号初始化：

#include <opencv2\opencv.hpp>
#include <iostream>
#include <string>

//#pragma comment( linker, "/subsystem:\"windows\" /entry:\"mainCRTStartup\"" )

using namespace std;
using namespace cv;

void Show(std::string name,Mat img)
{
namedWindow(name, CV_WINDOW_AUTOSIZE);
imshow(name, img);
}

int main()
{
Mat C = (Mat_<double>(3, 3) << 0, -1, 0, -1, 5, -1, 0, -1, 0);
cout << C << endl;
system("pause");
return 0;
}

可以按行列复制图像的色值：

#include <opencv2\opencv.hpp>
#include <iostream>
#include <string>

//#pragma comment( linker, "/subsystem:\"windows\" /entry:\"mainCRTStartup\"" )

using namespace std;
using namespace cv;

void Show(std::string name,Mat img)
{
namedWindow(name, CV_WINDOW_AUTOSIZE);
imshow(name, img);
}

int main()
{
Mat C = (Mat_<double>(3, 3) << 0, -1, 0, -1, 5, -1, 0, -1, 0);
Mat Row = C.col(1).clone();
cout << Row << endl;
system("pause");
return 0;
}

随机生成矩阵方式：

#include <opencv2\opencv.hpp>
#include <iostream>
#include <string>

#pragma comment( linker, "/subsystem:\"windows\" /entry:\"mainCRTStartup\"" )

using namespace std;
using namespace cv;

void Show(std::string name,Mat img)
{
namedWindow(name, CV_WINDOW_AUTOSIZE);
imshow(name, img);
}

int main()
{
Mat img;
img = Mat(400, 400, CV_8UC3);
randu(img, Scalar::all(0), Scalar::all(255));
Show("Img", img);
waitKey();
return 0;
}

randu后两个参数是取值范围；

矩阵值的输出格式：

#include <opencv2\opencv.hpp>
#include <iostream>
#include <string>

//#pragma comment( linker, "/subsystem:\"windows\" /entry:\"mainCRTStartup\"" )

using namespace std;
using namespace cv;

void Show(std::string name,Mat img)
{
namedWindow(name, CV_WINDOW_AUTOSIZE);
imshow(name, img);
}

int main()
{
Mat img;
img = Mat(4, 4, CV_8UC3,Scalar::all(25));
cout << img << endl;
cout << format(img, "python") << endl;
cout << format(img, "csv") << endl;
cout << format(img, "numpy") << endl;
cout << format(img, "C") << endl;
system("pause");
return 0;
}

其他可以直接用流输出的数据类型是

#include <opencv2\opencv.hpp>
#include <iostream>
#include <string>

//#pragma comment( linker, "/subsystem:\"windows\" /entry:\"mainCRTStartup\"" )

using namespace std;
using namespace cv;

void Show(std::string name,Mat img)
{
namedWindow(name, CV_WINDOW_AUTOSIZE);
imshow(name, img);
}

int main()
{
Point2f p2(5, 1);
cout << p2 << endl;
Point3f p3(2, 6, 7);
cout << p3 << endl;
vector<float> v;
v.push_back((float)CV_PI);
v.push_back(2);
v.push_back(3.01f);
cout << Mat(v) << endl;
vector<Point2f> vPoints(20);
for (size_t i = 0; i < vPoints.size(); ++i)
vPoints[i] = Point2f((float)(i * 5), (float)(i % 7));
cout << vPoints << endl;
system("pause");
return 0;
}

计算算法运行时间：

#include <opencv2\opencv.hpp>
#include <iostream>
#include <string>
#include <sstream>

//#pragma comment( linker, "/subsystem:\"windows\" /entry:\"mainCRTStartup\"" )

using namespace std;
using namespace cv;

void Show(std::string name,Mat img)
{
namedWindow(name, CV_WINDOW_AUTOSIZE);
imshow(name, img);
}

int main()
{
double t = getTickCount();
//do somethings
t = ((double)getTickCount() - t) / getTickFrequency();
cout << t << endl;
system("pause");
return 0;
}

下篇待续。。。

以上。