OpenMP在通过OpenCV处理图像时的应用

        大部分图像处理都是串行的（即该函数的输入来自于另一个函数的输出），OpenMP只能适用与图像独立处理的场合，比如对某文件夹中的图片进行相同的图像增强处理，或这里要说的对两张图像分别进行特征提取。

        OpenCV中使用Sift或者Surf特征进行图像拼接的算法，需要分别对两幅或多幅图像进行特征提取和特征描述，之后再进行图像特征点的配对，图像变换等操作。不同图像的特征提取和描述的工作是整个过程中最耗费时间的，也是独立 运行的，可以使用OpenMP进行加速。

        将#pragma omp注释掉即可对比没有采用OpenMP时的运行效率，在该程序中相当于是两个线程分别执行两幅图像的特征提取和描述操作。使用OpenMP后速度差不多提升了一倍。

#include "highgui/highgui.hpp"      
#include "opencv2/nonfree/nonfree.hpp"      
#include "opencv2/legacy/legacy.hpp"     
#include "omp.h"  

using namespace cv;

//计算原始图像点位在经过矩阵变换后在目标图像上对应位置    
Point2f getTransformPoint(const Point2f originalPoint, const Mat &transformMaxtri);

int main(int argc, char *argv[])
{
	float startTime = omp_get_wtime();

	Mat image01, image02;
	Mat image1, image2;
	vector keyPoint1, keyPoint2;
	Mat imageDesc1, imageDesc2;
	SiftFeatureDetector siftDetector(800);  // 海塞矩阵阈值    
	SiftDescriptorExtrwww.tt951.comactor siftDescriptor;
	//使用OpenMP的sections制导指令开启多线程  
#pragma omp parallel sections    
	{
#pragma omp section    
		{
			image01 = imread("Test01.jpg");
			imshow("拼接图像1", image01);
			//灰度图转换   
			cvtColor(image01, image1, CV_RGB2GRAY);
			//提取特征点    
			siftDetector.detect(image1, keyPoint1);
			//特征点描述，为下边的特征点匹配做准备      
			siftDescriptor.compute(image1, keyPoint1, imageDesc1);
		}
#pragma omp section    
		{
			image02 = imread("Test02.jpg");
			imshow("拼接图像2", image02);
			cvtColor(image02, image2, CV_RGB2GRAY);
			siftDetector.detect(image2, keyPoint2);
			siftDescriptor.compute(image2, keyPoint2, imageDesc2);
		}
	}
	float endTime = omp_get_wtime();
	std::cout << "使用OpenMP加速消耗时间： " << endTime - startTime << std::endl;

	//获得匹配特征点，并提取最优配对       
	FlannBasedMatcher matcher;
	vector matchePoints;
	matcher.match(imageDesc1, imageDesc2, matchePoints, Mat());
	sort(matchePoints.begin(), matchePoints.end()); //特征点排序      
	//获取排在前N个的最优匹配特征点    
	vector imagePoints1, imagePoints2;
	for (int i = 0; i < 10; i++)
	{
		imagePoints1.push_back(keyPoint1[matchePoints[i].queryIdx].pt);
		imagePoints2.push_back(keyPoint2[matchePoints[i].trainIdx].pt);
	}

	//获取图像1到图像2的投影映射矩阵，尺寸为3*3    
	Mat homo = findHomography(imagePoints1, imagePoints2, CV_RANSAC);
	Mat adjustMat = (Mat_(3, 3) << 1.0, 0, image01.cols, 0, 1.0, 0, 0, 0, 1.0);
	Mat adjustHomo = adjustMat * homo;

	//获取最强配对点在原始图像和矩阵变换后图像上的对应位置，用于图像拼接点的定位    
	Point2f originalLinkPoint, targetLinkPoint, basedImagePoint;
	originalLinkPoint = keyPoint1[matchePoints[0].queryIdx].pt;
	targetLinkPoint = getTransformPoint(originalLinkPoint, adjustHomo);
	basedImagePoint = keyPoint2[matchePoints[0].trainIdx].pt;

	//图像配准    
	Mat imageTransform1;
	warpPerspective(image01, imageTransform1, adjustMat*homo, Size(image02.cols + image01.cols + 110, image02.rows));

	//在最强匹配点左侧的重叠区域进行累加，是衔接稳定过渡，消除突变    
	Mat image1Overlap, image2Overlap; //图1和图2的重叠部分       
	image1Overlap = imageTransform1(Rect(Point(targetLinkPoint.x - basedImagePoint.x, 0), Point(targetLinkPoint.x, image02.rows)));
	image2Overlap = image02(Rect(0, 0, image1Overlap.cols, image1Overlap.rows));
	Mat image1ROICopy = image1Overlap.clone();  //复制一份图1的重叠部分   
	for (int i = 0; i < image1Overlap.rows; i++)
	{
		for (int j = 0; j < image1Overlap.cols; j++)
		{
			double weight;
			weight = (double)j / image1Overlap.cols;  //随距离改变而改变的叠加系数    
			image1Overlap.at(i, j)[0] = (1 - weight)*image1ROICopy.at(i, j)[0] + weight * image2Overlap.at(i, j)[0];
			image1Overlap.at(i, j)[1] = (1 - weight)*image1ROICopy.at(i, j)[1] + weight * image2Overlap.at(i, j)[1];
			image1Overlap.at(i, j)[2] = (1 - weight)*image1ROICopy.at(i, j)[2] + weight * image2Overlap.at(i, j)[2];
		}
	}
	Mat ROIMat = image02(Rect(Point(image1Overlap.cols, 0), Point(image02.cols, image02.rows)));  //图2中不重合的部分    
	ROIMat.copyTo(Mat(imageTransform1, Rect(targetLinkPoint.x, 0, ROIMat.cols, image02.rows))); //不重合的部分直接衔接上去    
	namedWindow("拼接结果", 0);
	imshow("拼接结果", imageTransform1);
	imwrite("D:\\拼接结果.jpg", imageTransform1);
	waitKey();
	return 0;
}

//计算原始图像点位在经过矩阵变换后在目标图像上对应位置    
Point2f getTransformPoint(const Point2f originalPoint, const Mat &transformMaxtri)
{
	Mat originelP, targetP;
	originelP = (Mat_(3, 1) << originalPoint.x, originalPoint.y, 1.0);
	targetP = transformwww.baiyuewang.netMaxtri*originelP;
	float x = targetP.at(0, 0) / targetP.at(2, 0);
	float y = targetP.at(1, 0) / targetP.at(2, 0);
	return Point2f(x, y);
}