Mat类创建二维、三维矩阵并访问以及opencv矩阵运算
一、opencv宽高对应关系：
Mat.rows = Mat.size().height = 高
Mat.cols = Mat.size().width = 宽
int sz_1[2] = { 200, 400 }; // {高，宽} {Mat.rows,Mat.cols}
Mat m = cv::Mat(2, sz_1, CV_8UC1,Scalar::all(255));
or
Mat m = cv::Mat::ones(2, sz_1, CV_8UC1);
or
Mat m = cv::Mat::zeros(200, 400, CV_8UC1); // 高：200 宽：400二、opencv矩阵创建以及元素级访问
#include
#include
#include
#include
using namespace std;
using namespace cv;
int main(){
Mat img = imread("123.jpg");
Mat gray;
cvtColor(img, gray, COLOR_BGR2GRAY);
// 创建单通道图片
int sz[2] = { gray.rows, gray.cols }; // {高，宽}
// 或者使用：Mat fgCount = Mat(2, sz, CV_8UC1, Scalar::all(0));
Mat fgCount = cv::Mat::zeros(2, sz, CV_8UC1);
cv::imshow("fgCount", fgCount);
cv::waitKey(2000);
fgCount.at(20, 20) = 20;
fgCount.at(21, 21) = 21;
std::cout << "hahhahh------------------" << std::endl;
std::cout << int(fgCount.at(20, 20)) << std::endl;
std::cout << int(fgCount.at(21, 21)) << std::endl;
// 创建三维矩阵
int samples_size[3];
int height = gray.size().height;
int width = gray.size().width;
samples_size[0] = 20;
samples_size[1] = gray.rows;
samples_size[2] = gray.cols;
Mat m_3 = cv::Mat::zeros(3, samples_size, CV_8UC1);
std::cout << "Yes" < Vec20f;
Mat m_2 = cv::Mat::zeros(gray.size().width, gray.size().height, CV_64FC(20));
std::cout << "Yes" << std::endl;
cout << "图像的维度：" << m_2.dims << endl;
cout << "图像的通道数：" << m_2.channels() << endl;
m_2.at(20, 20)[11] = 123.33;
cout << m_2.at(20, 20)[11] << endl;
// 遍历图片
Mat src = imread("haha.png");
Mat img = imread("haha.png");
Mat img_2 = imread("haha.png");
int nl = img.rows; //行数
int nc = img.cols * img.channels(); //每行元素的总元素数量
int div = 64;
int cols = img.cols;
// 采用指针
double start_time = (double)getTickCount();
for (int j = 0; j < nl; j++)
{
uchar* data = img.ptr(j);
for (int i = 0; i < cols; i++)
{
data[i* img.channels()] = 151;
}
}
double end_time = (double)getTickCount();
double fps = (end_time - start_time) / getTickCount();
cout << "采用指针所消耗时间：" << fps << endl;
// 采用内置at方法
start_time = (double)getTickCount();
for (int j = 0; j < nl; j++)
{
for (int i = 0; i < cols; i++)
{
img_2.at(j, i)[1] = 151;
}
}
end_time = (double)getTickCount();
fps = (end_time - start_time) / getTickCount();
cout << "采用内置at方法消耗时间：" < 
初始化Mat对象：
Mat grad_x = (Mat_(3, 3) << -1, 0, 1, -2, 0, 2, -1, 0, 1);
Mat grad_y = (Mat_(3, 3) << -1, -2, -1, 0, 0, 0, 1, 2, 1);
cout << grad_x << endl;
cout << grad_y << endl;
三、opencv矩阵运算
现在opencv的Mat类矩阵运算操作已经和numpy很像了，一些案例如下：
void matOP() {
Mat mat_1(4, 9, CV_16SC1);
Mat mat_2(4, 9, CV_16SC1);
Mat mat_3(4, 9, CV_16SC1);
RNG rng(unsigned int(time(NULL)));
rng.fill(mat_1, RNG::UNIFORM, -4, 4, true);
rng.fill(mat_2, RNG::UNIFORM, 0, 10, true);
mat_3 = mat_1 + mat_2;
cout << "mat_1:" << endl;
cout << mat_1 << endl;
cout << "mat_2:" << endl;
cout << mat_2 << endl;
cout << "mat_3:" << endl;
cout << mat_3 << endl;
Mat big_zero = Mat::zeros(4, 9, CV_16SC1);
big_zero = mat_3 >= 0;
cout << "big_zero:" << endl;
cout << big_zero << endl;
Mat ret_1 = Mat::zeros(4, 9, CV_16SC1);
bitwise_and(mat_3, mat_3, ret_1, big_zero);
cout << "ret_1:" << endl;
cout << ret_1;
int a;
cin >> a;
}结果截图：
四、opencv产生随机数
比RNG方便，如下（每次产生的随机是一样的）：
void rand_test() {
Mat R = Mat(3, 2, CV_16SC3);
// randu(dst, low, high);dst – 输出数组或矩阵 ；low – 区间下界（闭区间）； high - 区间上界（开区间）
randu(R, -4, 5); // 返回均匀分布的随机数，填入数组或矩阵
cout << R << endl;
// randn(dst, mean, stddev);dst – 输出数组或矩阵； mean – 均值； stddev - 标准差
randn(R, -4, 4); // 返回高斯分布的随机数，填入数组或矩阵
cout << R << endl;
/*randShuffle(InputOutputArray dst, 输入输出数组（一维）
double iterFactor = 1., 决定交换数值的行列的位置的一个系数...
RNG* rng = 0) （可选）随机数产生器，0表示使用默认的随机数产生器，即seed = -1。rng决定了打乱的方法*/
unsigned int seed = time(NULL);
RNG rng(seed);
randShuffle(R, 1, &rng); // 将原数组（矩阵）打乱
cout << R << endl;
}结果：
