OpenCV 从2.4.3开始加入了并行计算的函数parallel_for和parallel_for_（更准确地讲，parallel_for以前就存在于tbb模块中，但是OpenCV官网将其列在2.4.3.的New Features中，应该是重新改写过的）。
2.4.3中自带的calcOpticalFlowPyrLK函数也用parallel_for重写过了，之前我一直认为parallel_for就是用来并行计算的，之前也自己写了一些用parallel_for实现的算法。直到今天在opencv官网中看到别人的提问，才发现parallel_for实际上是serial loop，而parallel_for_才是parallel loop（OpenCV官网answer）。
为了比较for循环，parallel_for和parallel_for_ 三者的差异，下面做了一个简单的测试，对一个Mat中所有的元素（按列为单位）做立方操作。
Code
test.hpp
/**@ Test parallel_for and parallel_for_/**@ Author: chouclee/**@ 03/17/*/#include <opencv2/core/internal.hpp>namespace cv{namespace test{class parallelTestBody : public ParallelLoopBody//参考官方给出的answer，构造一个并行的循环体类{public:parallelTestBody(Mat& _src)//class constructor{src = &_src;}void operator()(const Range& range) const//重载操作符（）{Mat& srcMat = *src;int stepSrc = (int)(srcMat.step/srcMat.elemSize1());//获取每一行的元素总个数（相当于cols*channels，等同于step1)for (int colIdx = range.start; colIdx < range.end; ++colIdx){float* pData = (float*)srcMat.col(colIdx).data;for (int i = 0; i < srcMat.rows; ++i)pData[i*stepSrc] = std::pow(pData[i*stepSrc],3);}}private:Mat* src;};struct parallelTestInvoker//构造一个供parallel_for使用的循环结构体{parallelTestInvoker(Mat& _src)//struct constructor{src = &_src;}void operator()(const BlockedRange& range) const//使用BlockedRange需要包含opencv2/core/internal.hpp{Mat& srcMat = *src;int stepSrc = (int)(srcMat.step/srcMat.elemSize1());for (int colIdx = range.begin(); colIdx < range.end(); ++colIdx){float* pData = (float*)srcMat.col(colIdx).data;for (int i = 0; i < srcMat.rows; ++i)pData[i*stepSrc] = std::pow(pData[i*stepSrc],3);}}Mat* src;};}//namesapce testvoid parallelTestWithFor(InputArray _src)//'for' loop{CV_Assert(_src.kind() == _InputArray::MAT);Mat src = _src.getMat();CV_Assert(src.isContinuous());int stepSrc = (int)(src.step/src.elemSize1());for (int x = 0; x < src.cols; ++x){float* pData = (float*)src.col(x).data;for (int y = 0; y < src.rows; ++y)pData[y*stepSrc] = std::pow(pData[y*stepSrc], 3);}};void parallelTestWithParallel_for(InputArray _src)//'parallel_for' loop{CV_Assert(_src.kind() == _InputArray::MAT);Mat src = _src.getMat();int totalCols = src.cols;typedef test::parallelTestInvoker parallelTestInvoker;parallel_for(BlockedRange(0, totalCols), parallelTestInvoker(src));};void parallelTestWithParallel_for_(InputArray _src)//'parallel_for_' loop{CV_Assert(_src.kind() == _InputArray::MAT);Mat src = _src.getMat();int totalCols = src.cols;typedef test::parallelTestBody parallelTestBody;parallel_for_(Range(0, totalCols), parallelTestBody(src));};}//namespace cv
main.cpp
/**@ Test parallel_for and parallel_for_/**@ Author: chouclee/**@ 03/17/*/#include <opencv2/opencv.hpp>#include <time.h>#include "test.hpp"using namespace cv;using namespace std;int main(int argc, char* argv[]){Mat testInput = Mat::ones(40,400000, CV_32F);clock_t start, stop;start = clock();parallelTestWithFor(testInput);stop = clock();cout<<"Running time using \'for\':"<<(double)(stop - start)/CLOCKS_PER_SEC*1000<<"ms"<<endl;start = clock();parallelTestWithParallel_for(testInput);stop = clock();cout<<"Running time using \'parallel_for\':"<<(double)(stop - start)/CLOCKS_PER_SEC*1000<<"ms"<<endl;start = clock();parallelTestWithParallel_for_(testInput);stop = clock();cout<<"Running time using \'parallel_for_\':"<<(double)(stop - start)/CLOCKS_PER_SEC*1000<<"ms"<<endl;system("pause");}
Result 输入为400000*40时，结果如下：
Debug模式
Running time using 'for': 1376ms
Running time using 'parallel_for': 1316ms
Running time using 'parallel_for_': 553ms
Release模式
Running time using 'for': 463ms
Running time using 'parallel_for': 475ms
Running time using 'parallel_for_': 301ms
输入改为40*400000
Debug模式
Running time using 'for': 1005ms
Running time using 'parallel_for': 1013ms
Running time using 'parallel_for_': 526ms
Release模式
Running time using 'for': 105ms
Running time using 'parallel_for': 106ms
Running time using 'parallel_for_': 81ms
输入改为4000*4000
Debug模式
Running time using 'for': 1138ms
Running time using 'parallel_for': 1136ms
Running time using 'parallel_for_': 411ms
Release模式
Running time using 'for': 234ms
Running time using 'parallel_for': 239ms
Running time using 'parallel_for_': 130ms
大多数情况下，parallel_for比for循环慢那么一丁丁点儿，有时甚至会比for循环快一些，总体上两者差不多，parallel_for_一直都是最快的。但上面的代码只是做测试使用（因此强制按列进行操作），实际上，像上面这种简单的操作，直接对Mat使用for循环和指针递增操作，只需要几十毫秒。但是，对于复杂算法，比如光流或之类的，使用parallel_for（虽然不是并行操作，但代码简洁易于维护，且速度和for循环差不多）或者parallel_for_将是不错的选择。
Reference:
/question/3730/how-to-use-parallel_for/
