Question

我正在尝试比较OpenCV 3.0版中灰度图像的扫描图像像素的运行时间。我已经阅读了OpenCV文档how_to_scan_images

我理解cv::convertTo因各种优化而成为最快的。我也明白cpointer样式方法将是第二好的。但是，我对MatIterator（method3）和Mat::at运算符（方法2）之间的差异感到惊讶。文档how_to_scan_images提到MatIterator应该比Mat::at<>运算符更快，但我收到的结果却不同。我错过了什么吗？这个结果有望吗？在算法开发的初始阶段，我想使用MatIterator，因为它与STL迭代器相似，通常被认为是一种更安全的方式。

关于我做错的任何想法？我在Ubuntu上使用OpenCV 3.0。对于多次运行，下面显示的输出在+ - 0.5 ms内大致一致。

在下面的代码中，我有效地计算（newImage = alpha*oldImage +beta）并比较以下方法的性能

方法1：使用cv:: convertTo，时间以ms为单位= 0.709923

方法2：使用img.at<uchar>(row,col)，时间以ms为单位= 5.09625

方法3：使用MatIterator_<uchar> it，时间ms = 18.277

方法4：cpointer uchar * p = img.ptr<uchar>(row)，时间ms = 3.49983

方法5：cpointer uchar * src = img.data，时间ms = 3.28267

#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/core/utility.hpp>
#include <iostream>
#include <string>

using namespace std;
using namespace cv;

int main(int argc, char ** argv)
{
string imgname("baboon.jpg");
if (argc>1)
    imgname = argv[1];
Mat im = imread(imgname.c_str(), IMREAD_COLOR);
if (im.empty())
{ cout<<" Invalid image read, imgname =argv[1] = "<<imgname<<endl; 
    return -1;
}
Mat img;
cvtColor(im, img,COLOR_BGR2GRAY);
// method 1: using convertTo
Mat img1;
double alpha =1, beta =50;
double t = (double)(getTickCount());
img.convertTo(img1, img.type(),alpha, beta);    
t = ((double)getTickCount() - t)/getTickFrequency();
cout<<"Method 1: using convertTo, Time elasped in ms = "<<t*1000<<endl;
namedWindow("img1", WINDOW_AUTOSIZE);
imshow("img1", img1);
// Method2: using img.at<>(row, col) index
 Mat img2 = Mat::zeros(img.size(), img.type());
t = (double)(getTickCount());
 for (int row=0; row<img.rows; row++)
{ for (int col =0; col<img.cols; col++)
  { img2.at<uchar>(row,col) = (saturate_cast<uchar>) (alpha*img.at<uchar>   (row,col) + beta);
    }
}
t = ((double)getTickCount() - t)/getTickFrequency();
cout<<"Method2: using img.at<uchar>(row,col), Time in ms = "<<t*1000<<endl;

//Method3: using Matiterator for each image point
Mat img3 = Mat::zeros(img.size(),img.type());
t = (double)(getTickCount());
MatIterator_<uchar> it, itDest, end;
    itDest= img3.begin<uchar>(); end =img.end<uchar>();
for (it = img.begin<uchar>(); it!= end; it++,itDest++)
{ *itDest = (saturate_cast<uchar>)((*it)*alpha + beta);
}
t = ((double)getTickCount() - t)/getTickFrequency();
cout<<"Method3: using MatIterator_<uchar> it, Time in ms = "<<t*1000<<endl;
//Method4: using c-style pointer, char * p = img.ptr<uchar>(rowNum)
 Mat img4 = Mat::zeros(img.size(), img.type());
t = (double)(getTickCount());
for (int row =0; row<img.rows; row++)
{ uchar * srcPtr = img.ptr<uchar>(row);
   uchar * destPtr = img4.ptr<uchar>(row); 
 for (int col =0; col<img.cols; col++)
 { destPtr[col] = (saturate_cast<uchar>) (alpha* srcPtr[col] + beta);
 }
}
t = ((double)getTickCount() - t)/getTickFrequency();
cout<<"Method4: cpointer uchar * p = img.ptr<uchar>(row), Time in ms =  "<<t*1000<<endl;

// method 5: using the address given by img.data() and iterating untill the end
Mat img5 = Mat::zeros(img.size(), img.type());
t = (double)(getTickCount());
uchar * src = img.data;
uchar * dest =img5.data;
for (int i=0; i<img.rows*img.cols; i++)
    *(dest++) = (saturate_cast<uchar>)(alpha * (*(src++)) + beta);
t = ((double)getTickCount() - t)/getTickFrequency();
cout<<"Method5: cpointer uchar * src = img.data, Time in ms =  "<<t*1000<<endl;
return 0;

}

Answer 1

在我自己挖掘之后是结论：基于性能（从最高到最低）的图像扫描方法比较

尽可能使用库函数。
首选C指针进行更快的扫描（可以使用img.data（）获得几毫秒的增益但要小心！）
img.at＆LT;＆GT;是下一个最好的
MatIterator是最快的。

仅在发布模式下“Mat :: at”比“MatIterator”更快。 MatIterator通过附加检查带来安全系数。但是，在调试模式下，“MatIterator”比“Mat :: at”运算符

访问图像像素; MatIterator_＆lt;＆gt;的比较和Mat :: at operator

1 个答案: