(一)理論
(二)重要的API
Mat dst = Mat::zeros(src.size(),src.type());創建一張與原圖像大小類型相同的空白圖像,初始值爲0;
Saturate_cast(數據) 確保數據在0~255之間
/*************獲取圖像像素值*************/
Mat.at<Vec3b>(row,col)[0] //blue通道像素值
Mat.at<Vec3b>(row,col)[1] //green通道像素值
Mat.at<Vec3b>(row,col)[2] //red通道像素值
Mat.at<uchar>(row,col) //灰度圖像數值
(代碼部分)
#include <opencv2/opencv.hpp>
#include <iostream>
#include <math.h>
using namespace cv;
using namespace std;
int main(int argc, char** argv)
{
Mat src;
src = imread("D:/picture/Curry.jpeg");
if (src.empty()) //如果沒有找到圖片
{
printf("could not find picture.....\n");
return -1;
}
//cvtColor(src, src, CV_BGR2GRAY); //轉化成灰度圖
namedWindow("input image", CV_WINDOW_AUTOSIZE);
imshow("input image", src);
Mat dst = src.zeros(src.size(), src.type());
int height = src.rows;
int weight = src.cols;
double alpha = 2;
double beta = 5;
for (int row = 0; row < height; row++)
{
for (int col = 0; col < weight; col++)
{
if (src.channels() == 3)
{
int blue = src.at<Vec3b>(row, col)[0];
int green = src.at<Vec3b>(row, col)[1];
int red = src.at<Vec3b>(row, col)[2];
dst.at<Vec3b>(row, col)[0] = saturate_cast<uchar>(blue * alpha + beta);
dst.at<Vec3b>(row, col)[1] = saturate_cast<uchar>(green * alpha + beta);
dst.at<Vec3b>(row, col)[2] = saturate_cast<uchar>(red * alpha + beta);
}
else if (src.channels() == 1)
{
int gray = src.at<uchar>(row, col);
dst.at<uchar>(row,col) = saturate_cast<uchar>(gray * alpha + beta);
}
}
}
namedWindow("output image", CV_WINDOW_AUTOSIZE);
imshow("output image", dst);
waitKey(0); //等待
return 0;
}
實驗效果