opencv 源码mat_mask_operations.cpp

最新推荐文章于 2024-06-26 15:47:24 发布

原创最新推荐文章于 2024-06-26 15:47:24 发布 · 280 阅读

0 ·

CC 4.0 BY-SA版权

本文介绍了一种使用自定义函数和OpenCV的filter2D函数进行图像锐化的实现方法。通过对比两种方法的时间消耗，展示了如何在C++中利用OpenCV库进行图像处理，并提供了完整的源代码示例。

摘要生成于 C知道，由 DeepSeek-R1 满血版支持，前往体验 >

#include <opencv2/imgcodecs.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/imgproc.hpp>
#include <iostream>

using namespace std;
using namespace cv;

static void help(char* progName)
{
   cout << endl
       << "This program shows how to filter images with mask: the write it yourself and the"
       << "filter2d way. " << endl
       << "Usage:" << endl
       << progName << " [image_path -- default ../data/lena.jpg] [G -- grayscale] " << endl << endl;
}

void Sharpen(const Mat& myImage, Mat& Result);

int main(int argc, char* argv[])
{
help(argv[0]);
const char* filename = argc >= 2 ? argv[1] : "../data/lena.jpg"; //如果没有输入图片就从默认位置读取

Mat src, dst0, dst1;

   if (argc >= 3 && !strcmp("G", argv[2])) //根据是否有G来改变imread函数参数
       src = imread(filename, IMREAD_GRAYSCALE);
   else
       src = imread(filename, IMREAD_COLOR);

   if (src.empty()) //是否成功读入图片
   {
       cerr << "Can't open image [" << filename << "]" << endl;
       return -1;
   }

namedWindow("Input", WINDOW_AUTOSIZE); //开两个窗口
namedWindow("Output", WINDOW_AUTOSIZE);

imshow("Input", src); //展示输入
double t = (double)getTickCount();

Sharpen(src, dst0); //调用手写函数

t = ((double)getTickCount() - t) / getTickFrequency();
cout << "Hand written function time passed in seconds: " << t << endl;

imshow("Output", dst0); //展示输出
waitKey();

   //![kern]
   Mat kernel = (Mat_<char>(3, 3) << 0, -1, 0, //表示掩码的Mat类
       -1, 5, -1,
       0, -1, 0);
   //![kern]

t = (double)getTickCount();

   //![filter2D]
   filter2D(src, dst1, src.depth(), kernel); //调用filter2D函数
   //![filter2D]
   t = ((double)getTickCount() - t) / getTickFrequency();
   cout << "Built-in filter2D time passed in seconds: " << t << endl;

imshow("Output", dst1); //展示输出

   waitKey();
   return 0;
}
//! [basic_method]
void Sharpen(const Mat& myImage, Mat& Result)
{
   //! [8_bit]
   CV_Assert(myImage.depth() == CV_8U); // accept only uchar images
                                       //! [8_bit]

                                       //! [create_channels]
   const int nChannels = myImage.channels();
   Result.create(myImage.size(), myImage.type());
   //! [create_channels]

   //! [basic_method_loop]
   for (int j = 1; j < myImage.rows - 1; ++j)
   {
       const uchar* previous = myImage.ptr<uchar>(j - 1); //记录上下行地址
       const uchar* current = myImage.ptr<uchar>(j);
       const uchar* next = myImage.ptr<uchar>(j + 1);

uchar* output = Result.ptr<uchar>(j);

       for (int i = nChannels; i < nChannels*(myImage.cols - 1); ++i)
       {
           *output++ = saturate_cast<uchar>(5 * current[i] //根据公式
               - current[i - nChannels] - current[i + nChannels] - previous[i] - next[i]);
       }
   }
   //! [basic_method_loop]

   //! [borders] //边界变成0
   Result.row(0).setTo(Scalar(0));
   Result.row(Result.rows - 1).setTo(Scalar(0));
   Result.col(0).setTo(Scalar(0));
   Result.col(Result.cols - 1).setTo(Scalar(0));
   //! [borders]
}
//! [basic_method]
这个算法是不是就是为了模糊呢？感觉和上一个很相似嘛