记录图像处理算法中运行效率优化的一些事20221016

图像处理系列文章目录

文章目录

  • 图像处理系列文章目录
  • 前言
  • 一、遍历图像像素的方法比对
  • 二、使用OpenMP加速
  • 总结

前言

记录图像处理中效率优化的一些过程
一些参考链接:
https://blog.csdn.net/libaineu2004/article/details/104129127
https://blog.csdn.net/qq_27278957/article/details/84646948

一、遍历图像像素的方法比对

以遍历所有像素对图像进行反转为例

#include "ImageProcess.h"

void PrintCostTime(double& t1, double& t2)
{
	double t = ((t2 - t1) / getTickFrequency()) * 1000;
	cout << "time: " << t << endl;
}
void method_at(Mat& _src)
{
	Mat src = _src.clone();
	double t1 = getTickCount();
	int w = src.cols;
	int h = src.rows;
	int dim = src.channels();
	for (int row = 0; row < h; row++) 
	{
		for (int col = 0; col < w; col++) 
		{
			if (dim == 3) {
				Vec3b bgr = src.at<Vec3b>(row, col);
				bgr[0] = 255 - bgr[0];
				bgr[1] = 255 - bgr[1];
				bgr[2] = 255 - bgr[2];
				src.at<Vec3b>(row, col) = bgr;
			}
			else if (dim == 1) {
				float pixel = src.at<uchar>(row, col);
				src.at<uchar>(row, col) =saturate_cast<uchar>(255 - pixel);
			}
		}
	}
	double t2 = getTickCount();
	PrintCostTime(t1, t2);
	imshow("result", src);
	waitKey(0);
}

void method_Matptr(Mat& _src)
{
	Mat src = _src.clone();
	double t1 = getTickCount();
	int w = src.cols;
	int h = src.rows;
	int dim = src.channels();
	if (dim == 3) {
		for (int row = 0; row < h; row++)
		{
			//uchar* pixel = src.ptr(row);
			Vec3b* pixel = src.ptr<cv::Vec3b>(row);
			for (int col = 0; col < w; col++)
			{
				//pixel[0] = 255 - pixel[0];
				//pixel[1] = 255 - pixel[1];
				//pixel[2] = 255 - pixel[2];
				//pixel += 3;
				//Vec3b bgr = pixel[col];
				pixel[col][0] = 255 - pixel[col][0];
				pixel[col][1] = 255 - pixel[col][1];
				pixel[col][2] = 255 - pixel[col][2];
			}
		}
	}
	else if (dim == 1) {
		for (int row = 0; row < h; row++)
		{
			uchar* pixel = src.ptr<uchar>(row);
			for (int col = 0; col < w; col++)
			{
				pixel[0] = 255 - pixel[0];
				pixel ++;
				//pixel[col] = 255 - pixel[col];
				//*pixel++ = 255 - *pixel;
			}
		}
	}
	double t2 = getTickCount();
	PrintCostTime(t1, t2);
	imshow("result", src);
	waitKey(0);
}

void method_Dataptr(Mat& _src)
{
	Mat src = _src.clone();
	double t1 = getTickCount();
	int w = src.cols;
	int h = src.rows;
	int dim = src.channels();
	if (dim == 3) {
		for (int row = 0; row < h; row++)
		{
			uchar* pixel = src.data + row*src.step;
			for (int col = 0; col < w; col++)
			{
				pixel[0] = 255 - pixel[0];
				pixel[1] = 255 - pixel[1];
				pixel[2] = 255 - pixel[2];
				pixel += 3;
			}
		}
	}
	else if (dim == 1) {
		for (int row = 0; row < h; row++)
		{
			uchar* pixel = src.data + row * src.step;
			for (int col = 0; col < w; col++)
			{
				pixel[0] = 255 - pixel[0];
				pixel++;
				//pixel[col] = 255 - pixel[col];
				//*pixel++ = 255 - *pixel;
			}
		}
	}
	double t2 = getTickCount();
	PrintCostTime(t1, t2);
	imshow("result", src);
	waitKey(0);
}

void method_iterator(Mat& _src)
{
	Mat src = _src.clone();
	double t1 = getTickCount();
	int w = src.cols;
	int h = src.rows;
	int dim = src.channels();
	if (dim == 3) {
		Mat_<Vec3b>::iterator it = src.begin<Vec3b>();
		Mat_<Vec3b>::iterator itend = src.end<Vec3b>();
		for (; it != itend; ++it)
		{
			(*it)[0] = 255 - (*it)[0];
			(*it)[1] = 255 - (*it)[1];
			(*it)[2] = 255 - (*it)[2];
		}
	}
	if (dim == 1) {
		Mat_<uchar>::iterator it = src.begin<uchar>();
		Mat_<uchar>::iterator itend = src.end<uchar>();
		for (; it != itend; ++it)
		{
			(*it) = 255 - (*it);
		}
	}
	double t2 = getTickCount();
	PrintCostTime(t1, t2);
	imshow("result", src);
	waitKey(0);
}

可以看到,使用指针的方法最快
记录图像处理算法中运行效率优化的一些事20221016_第1张图片

二、使用OpenMP加速

记录图像处理算法中运行效率优化的一些事20221016_第2张图片
如果是Windows Visual Studio在属性里面设置开启openmp,如果是Linux的话, CMakeLists.txt加上配置:

find_package(OpenMP REQUIRED)
if (OPENMP_FOUND)
	message("OPENMP FOUND")
    set (CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OpenMP_C_FLAGS}")
    set (CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}")
    set (CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} ${OpenMP_EXE_LINKER_FLAGS}")
endif()

修改代码,在for循环处加上#pragma omp parallel for num_threads(4) 这一句

#include "ImageProcess.h"

void PrintCostTime(double& t1, double& t2)
{
	double t = ((t2 - t1) / getTickFrequency()) * 1000;
	cout << "time: " << t << endl;
}
void method_at(Mat& _src)
{
	Mat src = _src.clone();
	double t1 = getTickCount();
	int w = src.cols;
	int h = src.rows;
	int dim = src.channels();
#pragma omp parallel for num_threads(4)  //指定4个线程
	for (int row = 0; row < h; row++) 
	{
		for (int col = 0; col < w; col++) 
		{
			if (dim == 3) {
				Vec3b bgr = src.at<Vec3b>(row, col);
				bgr[0] = 255 - bgr[0];
				bgr[1] = 255 - bgr[1];
				bgr[2] = 255 - bgr[2];
				src.at<Vec3b>(row, col) = bgr;
			}
			else if (dim == 1) {
				float pixel = src.at<uchar>(row, col);
				src.at<uchar>(row, col) =saturate_cast<uchar>(255 - pixel);
			}
		}
	}
	double t2 = getTickCount();
	cout << "method_at ";
	PrintCostTime(t1, t2);
	imshow("result", src);
	waitKey(0);
}

void method_Matptr(Mat& _src)
{
	Mat src = _src.clone();
	double t1 = getTickCount();
	int w = src.cols;
	int h = src.rows;
	int dim = src.channels();
	if (dim == 3) {
#pragma omp parallel for num_threads(4)  //指定4个线程
		for (int row = 0; row < h; row++)
		{
			//uchar* pixel = src.ptr(row);
			Vec3b* pixel = src.ptr<cv::Vec3b>(row);
			for (int col = 0; col < w; col++)
			{
				//pixel[0] = 255 - pixel[0];
				//pixel[1] = 255 - pixel[1];
				//pixel[2] = 255 - pixel[2];
				//pixel += 3;
				//Vec3b bgr = pixel[col];
				pixel[col][0] = 255 - pixel[col][0];
				pixel[col][1] = 255 - pixel[col][1];
				pixel[col][2] = 255 - pixel[col][2];
			}
		}
	}
	else if (dim == 1) {
#pragma omp parallel for num_threads(4)  //指定4个线程
		for (int row = 0; row < h; row++)
		{
			uchar* pixel = src.ptr<uchar>(row);
			for (int col = 0; col < w; col++)
			{
				pixel[0] = 255 - pixel[0];
				pixel ++;
				//pixel[col] = 255 - pixel[col];
				//*pixel++ = 255 - *pixel;
			}
		}
	}
	double t2 = getTickCount();
	cout << "method_Matptr ";
	PrintCostTime(t1, t2);
	imshow("result", src);
	waitKey(0);
}

void method_Dataptr(Mat& _src)
{
	Mat src = _src.clone();
	double t1 = getTickCount();
	int w = src.cols;
	int h = src.rows;
	int dim = src.channels();
	if (dim == 3) {
#pragma omp parallel for num_threads(4)  //指定4个线程
		for (int row = 0; row < h; row++)
		{
			uchar* pixel = src.data + row*src.step;
			for (int col = 0; col < w; col++)
			{
				pixel[0] = 255 - pixel[0];
				pixel[1] = 255 - pixel[1];
				pixel[2] = 255 - pixel[2];
				pixel += 3;
			}
		}
	}
	else if (dim == 1) {
#pragma omp parallel for num_threads(4)  //指定4个线程
		for (int row = 0; row < h; row++)
		{
			uchar* pixel = src.data + row * src.step;
			for (int col = 0; col < w; col++)
			{
				pixel[0] = 255 - pixel[0];
				pixel++;
				//pixel[col] = 255 - pixel[col];
				//*pixel++ = 255 - *pixel;
			}
		}
	}
	double t2 = getTickCount();
	cout << "method_Dataptr ";
	PrintCostTime(t1, t2);
	imshow("result", src);
	waitKey(0);
}

再次测试速度,可以看到速度变快了一点,是y因为for循环处耗时本身很短,如果换成自己的算法的话,速度还是可以提升非常多的,我自己的项目中优化后从700多ms到现在120ms提升效果非常nice。
记录图像处理算法中运行效率优化的一些事20221016_第3张图片

总结

图像处理过程的简单记录

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