OpenCV4.x中新增并行代码执行演示

原创 OpenCV学堂 2024-02-18 23:15

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OpenCV支持的并行框架

OpenCV从4.5版本开始，新增了并行代码执行支持，以常见的图像像素遍历卷积计算为例，演示OpenCV中卷积计算并行代码执行与非并行的卷积计算代码执行，同时对比时间消耗。OpenCV并行框架支持下面几种方法启用并行加速，分别是：

1. Intel TBB (第三方库，需显式启用)2. C=并行C/C++编程语言扩展 (第三方库，需显式启用)3. OpenMP (编译器集成, 需显式启用)4. APPLE GCD (苹果系统自动使用)5. Windows RT并发(Windows RT自动使用)6. Windows并发(运行时部分, Windows，MSVC++ >= 10自动使用)7. Pthreads

在VS IDE中开启OpenMP，只需要右键点击项目，从属性中

这样就可以开启并行加速。

卷积并行实现与时间比较

OpenCV支持两种方式的并行代码实现，分别是：

parallel_for_ParallelLoopBody

以3x3卷积为例，原始的代码实现如下：

start = (double)cv::getTickCount();for (int row = 0; row < rows; row++) {  for (int col = 1; col < cols - 1; col++)  {    int sum = src.at(row, col) + src.at(row - 1, col) + src.at(row + 1, col) +      src.at(row, col - 1) + src.at(row - 1, col - 1) + src.at(row + 1, col - 1) +      src.at(row, col + 1) + src.at(row - 1, col + 1) + src.at(row + 1, col + 1);    int pv = sum / 9;    dst.at(row, col) = pv;  }}

parallel_for_ 3x3卷积的代码实现如下

double start = (double)cv::getTickCount();parallel_for_(Range(0, rows * cols), [&](const Range &range){  for (int r = range.start; r < range.end; r++)  {    int i = r / cols, j = r % cols;    double value = 0;    for (int k = -sz; k <= sz; k++)    {      uchar *sptr = src.ptr(i + sz + k);      for (int l = -sz; l <= sz; l++)      {        value += kernel.ptr<double>(k + sz)[l + sz] * sptr[j + sz + l];      }    }    dst.ptr(i)[j] = saturate_cast(value);  }});double time = (((double)cv::getTickCount() - start)) / cv::getTickFrequency();std::cout << "parallel_for_conv3x3 execute time: " << time * 1000 << " ms" << std::endl;

ParallelLoopBody 3x3卷积的代码实现如下

class parallelConvolution : public ParallelLoopBody{private:  Mat m_src, &m_dst;  Mat m_kernel;  int sz;public:  parallelConvolution(Mat src, Mat &dst, Mat kernel)  : m_src(src), m_dst(dst), m_kernel(kernel)  {    sz = kernel.rows / 2;  }  virtual void operator()(const Range &range) const CV_OVERRIDE  {    for (int r = range.start; r < range.end; r++)    {      int i = r / m_src.cols, j = r % m_src.cols;      double value = 0;      for (int k = -sz; k <= sz; k++)      {        const uchar *sptr = m_src.ptr(i + sz + k);        for (int l = -sz; l <= sz; l++)        {          value += m_kernel.ptr<double>(k + sz)[l + sz] * sptr[j + sz + l];        }      }      m_dst.ptr(i)[j] = saturate_cast(value);    }  }};

调用方式如下：

start = (double)cv::getTickCount();parallelConvolution obj(src, dst, kernel);parallel_for_(Range(0, rows * cols), obj);time = (((double)cv::getTickCount() - start)) / cv::getTickFrequency();  std::cout << "parallelConvolution conv3x3 execute time: " << time * 1000 << " ms" << std::endl;