1.Mat 对象和IPImage 对象
IPImage 内部存在内存泄漏问题，因此一般不用此对象
mat类常用的构造函数： Mat::Mat() Mat::Mat(int rows, int cols, int type) Mat::Mat(Size size, int type） Mat::Mat(int rows, int cols, int type, const Scalar& s) Mat::Mat(Size size, int type, const Scalar& s) Mat::Mat(const Mat& m) Mat::Mat(int rows, int cols, int type, void* data, size_t step=AUTO_STEP) Mat::Mat(Size size, int type, void* data, size_t step=AUTO_STEP) Mat::Mat(const Mat& m, const Range& rowRange, const Range& colRange)
补充使用方法：
M4 = (Mat_(3, 3) << 0, -1, 0, -1, 5, -1, 0, -1, 0);
Mat M8 = Mat::zeros(src.size(), src.type());
案例说明：
Mat M(100, 100, CV_8UC1, Scalar(127));
cout << "M=" << M << endl;
Mat M2(10, 10, CV_8UC3, Scalar(0, 0, 255));
cout << "M1=" << M2 << endl;
Mat M3;
M3.create(src.size(), src.type());
M3 = Scalar(0, 255, 0);
Mat M4;
M4 = (Mat_<char>(3, 3) << 0, -1, 0, -1, 5, -1, 0, -1, 0);
Mat M5 = Mat::eye(src.size(), src.type());
Mat M6 = Mat::eye(2, 2, src.type());
Mat M7 = Mat::eye(3, 3, CV_8UC3);
Mat M8 = Mat::zeros(src.size(), src.type());
Mat M9 = Mat::zeros(2, 2, CV_8UC2);
其中scalar的使用为进行通道赋值
2.常用简单方法属性：
clone copyto ptr channels cols rows
Mat dst;
src.copyTo(dst);
printf("the first row pixel value is %d\n ", dst.ptr<char>(0));
printf(" rows : %d\n", dst.rows);
printf(" cols : %d\n", dst.cols);
printf("channels of dst:%d\n", dst.channels());
3.使用腌模进行图像的增强处理
#include "pch.h"
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/opencv.hpp>
#include<iostream>
#include<math.h>
using namespace std;
using namespace cv;
int main()
{
Mat src,dst;
src = imread("./test.png");
if (!src.data)
{
printf("There is no photo!\n");
return -1;
}
namedWindow("input_image", CV_WINDOW_AUTOSIZE);
imshow("input_image", src);
//使用指针读取对应行指针
/***
image.ptr<uchar>(row)[col] ;第row行第col列像素的指针 ptr point row
saturate_cast<uchar>(int) 饱和去除 防止超过 0-255
****/
int cols = (src.cols-1) * src.channels();
int offsetx = src.channels();
int rows = src.rows;
dst = Mat::zeros(src.size(), src.type());
for (int row = 1; row < (rows - 1); row++) {
const uchar* previous = src.ptr<uchar>(row - 1);
const uchar* current = src.ptr<uchar>(row);
const uchar* next = src.ptr<uchar>(row + 1);
uchar* output = dst.ptr<uchar>(row);
for (int col = offsetx; col < cols; col++) {
output[col] = saturate_cast<uchar>(5 * current[col] - (current[col- offsetx] + current[col+ offsetx] + previous[col] + next[col]));
}
}
//使用腌模方式
double t = getTickCount(); //获取操作系统开始到现在经过的操作数 用于计时，也可用clock_t代替
Mat kernel = (Mat_<char>(3, 3) << 0, -1, 0, -1, 5, -1, 0, -1, 0);//常见的初始化核函数的方法
filter2D(src, dst, src.depth(), kernel);
double timeconsume = (getTickCount() - t) / getTickFrequency();
printf("time consume %.2f\n", timeconsume);
namedWindow("output_image", CV_WINDOW_AUTOSIZE);
imshow("output_image", dst);
waitKey(0);
return 0;
}
