OpenCV算法加速(4)官方源码v4.5.5的默认并行和优化加速的编译选项是什

系统教程610 更新时间：2026-04-04 15:36:05

2023年12月13日发(作者：)

OpenCV算法加速（4）官方源码v4.5.5的默认并行和优化加速

的编译选项是什么？请重点。。。

举例opencv v4.5.5版本源码，Windows x64，VS2019，CMake

下载安装opencv-4.5.5-vc14_，得到官方编译的动态库opencv_

1、多线程并行库的使用情况

PPL

，里面默认使用的是微软PPL加速方式，

#if defined _MSC_VER && _MSC_VER >= 1600

#define HAVE_CONCURRENCY

#endif

cmake里面的编译选项，其他方式如TBB，HPX，OPENMP默认是关闭的。

opencvsourcesx64cvconfig.h，里面有宏定义的。

/* Halide support */

/* #undef HAVE_HALIDE */

/* Intel Integrated Performance Primitives */

/* #undef HAVE_IPP */

/* #undef HAVE_IPP_ICV */

/* #undef HAVE_IPP_IW */

/* #undef HAVE_IPP_IW_LL */

/* Intel Threading Building Blocks */

/* #undef HAVE_TBB */

/* Ste||ar Group High Performance ParallelX */

/* #undef HAVE_HPX */

/* OpenVX */

/* #undef HAVE_OPENVX */

2、GPU之OpenCL

cmake里面的编译选项，默认是开启OpenCL的。

opencvsourcesx64cvconfig.h

/* OpenCL Support */

#define HAVE_OPENCL

/* #undef HAVE_OPENCL_STATIC */

/* #undef HAVE_OPENCL_SVM */

/* NVIDIA OpenCL D3D Extensions support */

#define HAVE_OPENCL_D3D11_NV

GPU之CUDA

cmake里面的编译选项，默认是禁用CUDA的。需要用户自己勾选，并下载库编译。

3、SIMD指令集优化选项

cmake编译选项，opencvsourcesx64cvconfig.h，宏定义CV_ENABLE_INTRINSICS默认是开启的

#ifndef OPENCV_CVCONFIG_H_INCLUDED

#define OPENCV_CVCONFIG_H_INCLUDED

/* OpenCV compiled as static or dynamic libs */

#define BUILD_SHARED_LIBS

/* OpenCV intrinsics optimized code */

#define CV_ENABLE_INTRINSICS

/* OpenCV additional optimized code */

/* #undef CV_DISABLE_OPTIMIZATION */

cmake编译选项，opencvsourcesx64cv_cpu_config.h

// OpenCV CPU baseline features

#define CV_CPU_COMPILE_SSE 1

#define CV_CPU_BASELINE_COMPILE_SSE 1

#define CV_CPU_COMPILE_SSE2 1

#define CV_CPU_BASELINE_COMPILE_SSE2 1

#define CV_CPU_COMPILE_SSE3 1

#define CV_CPU_BASELINE_COMPILE_SSE3 1

#define CV_CPU_BASELINE_FEATURES 0

, CV_CPU_SSE

, CV_CPU_SSE2

, CV_CPU_SSE3

// OpenCV supported CPU dispatched features

#define CV_CPU_DISPATCH_COMPILE_SSE4_1 1

#define CV_CPU_DISPATCH_COMPILE_SSE4_2 1

#define CV_CPU_DISPATCH_COMPILE_FP16 1

#define CV_CPU_DISPATCH_COMPILE_AVX 1

#define CV_CPU_DISPATCH_COMPILE_AVX2 1

#define CV_CPU_DISPATCH_COMPILE_AVX512_SKX 1

#define CV_CPU_DISPATCH_FEATURES 0

, CV_CPU_SSE4_1

, CV_CPU_SSE4_2

, CV_CPU_FP16

, CV_CPU_AVX

, CV_CPU_AVX2

, CV_CPU_AVX512_SKX

关于OpenCV硬件加速，OpenCV Hardware Acceleration Layer(HAL)，请参考博文：

4、IPP加速

Intel® Integrated Performance Primitives (简称Intel® IPP)是一个软件库，提供了大量的函数。包括信号处理、图像处理、计算机视

觉、数据压缩和字符串操作。通过对函数的优化，比如适配指令集操作等来提升运行效率。

完整版的IPP下载地址：

在

Learning OpenCV

这本书中，作者提到OpenCV可以利用Intel的IPP性能库来提升程序的运行速度，而这个IPP库是要另外进行购买

的。实际上，Intel为当前的OpenCV免费提供了IPP加速库的一部分，在此我们称之为ippicv。ippicv会在cmake的时候自动从github上下

载，但是在网络状况不佳的情况下会下载失败。这时候我们只能采用手动安装的方式。ippicv的下载地址其实就藏在文件中。

里面的变量组合起来就是ippicv的下载网址：

或者直接访问这个网址也可以获得：

如果在英特尔的处理器上使用，OpenCV就会自动使用一种免费的英特尔集成性能原语库（IPP）的子集，IPP 8.x(IPPICV)。IPPICV可以

在编译阶段链接到OpenCV，这样一来，会替代相应的低级优化的C语言代码（在cmake中设置WITH_IPP=ON/OFF开启或者关闭这一功

能，默认情况为开启）。使用IPP获得的速度提升非常可观，如下图显示了使用IPP之后得到的加速效果。 5、怎么知道当前OpenCV开启了哪些加速模式？源码，有专门的函数设置和查看优化的情况：volatile bool useOptimizedFlag = true;void setUseOptimized( bool flag ){ useOptimizedFlag = flag; currentFeatures = flag ? &featuresEnabled : &featuresDisabled; ipp::setUseIPP(flag);#ifdef HAVE_OPENCL ocl::setUseOpenCL(flag);#endif}bool useOptimized(void){ return useOptimizedFlag;}void setUseIPP(bool flag){ CoreTLSData& data = getCoreTlsData();#ifdef HAVE_IPP = (getIPPSingleton().useIPP)?flag:false;#else CV_UNUSED(flag); = false;#endif}

void setUseOpenCL(bool flag)

{

if (!flag)

useOpenCL_ = 0;

else

useOpenCL_ = -1;

}

已知宏HAVE_OPENCL是已定义的，HAVE_IPP也是已定义的。所以OpenCL和IPP的功能默认是启动的。

可以通过以下测试用例，来查询当前的opencv是否开启了OPENCL和IPP

#include

void checkIPP()

{

bool use = cv::ipp::useIPP();

bool useNE = cv::ipp::useIPP_NotExact();

int status = cv::ipp::getIppStatus();

cv::String ver = cv::ipp::getIppVersion();

cv::String loc = cv::ipp::getIppErrorLocation();

std::cout << "IPP use:" << use << std::endl;

std::cout << "IPP useNE:" << useNE << std::endl;

std::cout << "IPP status:" << status << std::endl;

std::cout << "IPP ver:" << ver << std::endl;

std::cout << "IPP loc:" << loc << std::endl;

}

void checkOCL()

{

//查询opencv当前是否开启了OpenCL功能

bool ret1 = cv::ocl::haveOpenCL();

bool ret2 = cv::ocl::useOpenCL();

std::cout << "default haveOpenCL:" << ret1 << std::endl;

std::cout << "default useOpenCL:" << ret2 << std::endl;

}

6、最后，举例来看看canny算子的优化情况是什么？

，源码有宏定义#if CV_SIMD，

如果使能了宏定义CV_SIMD，就会开启指令集的优化源码。CV_SIMD默认是开启的，在头文件

，有定义

#if (CV_SSE2 || CV_NEON || CV_VSX || CV_MSA || CV_WASM_SIMD || CV_RVV071 || CV_RVV) && !defined(CV_FORCE_SIMD128_CPP)

#define CV__SIMD_FORWARD 128

#include "opencv2/core/hal/intrin_"

#endif

#if CV_SSE2 && !defined(CV_FORCE_SIMD128_CPP)

#include "opencv2/core/hal/intrin_sse_"

#include "opencv2/core/hal/intrin_"

namespace CV__SIMD_NAMESPACE {

#define CV_SIMD CV_SIMD128

另外，canny算子默认是开启了PPL并行计算的，并且也默认开启了对OpenCL和IPP的支持，一起来看看源码

，

void Canny( InputArray _src, OutputArray _dst,

double low_thresh, double high_thresh,

int aperture_size, bool L2gradient )

{

......

CV_OCL_RUN(_() && (_ls() == 1 || _ls() == 3),

ocl_Canny(_src, UMat(), UMat(), _dst, (float)low_thresh, (float)high_thresh, aperture_size, L2gradient, _ls(), size))

......

CALL_HAL(canny, cv_hal_canny, , , , , , , ls(),

low_thresh, high_thresh, aperture_size, L2gradient);

......

CV_IPP_RUN_FAST(ipp_Canny(src, Mat(), Mat(), dst, (float)low_thresh, (float)high_thresh, L2gradient, aperture_size))

......

parallel_for_(Range(0, ), parallelCanny(src, map, stack, low, high, aperture_size, L2gradient), numOfThreads);

......

}

void Canny( InputArray _dx, InputArray _dy, OutputArray _dst,

double low_thresh, double high_thresh,

bool L2gradient )

{

......

CV_OCL_RUN(_(),

ocl_Canny(UMat(), _t(), _t(), _dst, (float)low_thresh, (float)high_thresh, 0, L2gradient, _ls(), size))

......

CV_IPP_RUN_FAST(ipp_Canny(Mat(), dx, dy, dst, (float)low_thresh, (float)high_thresh, L2gradient, 0))

......

parallel_for_(Range(0, ), parallelCanny(dx, dy, map, stack, low, high, L2gradient), numOfThreads);

......

}

opencvsourcesmodulesimgprocsrchal_

inline int hal_ni_canny(const uchar* src_data, size_t src_step, uchar* dst_data, size_t dst_step,

int width, int height, int cn, double lowThreshold, double highThreshold, int ksize, bool L2gradient)

{ return CV_HAL_ERROR_NOT_IMPLEMENTED; }

//! @cond IGNORED

#define cv_hal_canny hal_ni_canny

//! @endcond

注意：

（1）CV_OCL_RUN有参数_()，表明OpenCL需要使用UMat作为图像格式的输入，否则会失效；

（2）CALL_HAL是硬件加速，但是深入源码却发现返回值是CV_HAL_ERROR_NOT_IMPLEMENTED，表明该功能官方暂时未实现。

（3）以上两个如果都不执行，那么就执行CV_IPP_RUN_FAST(

#define CV_IPP_RUN_(condition, func, ...)

{

if (cv::ipp::useIPP() && (condition))

{

CV__TRACE_REGION_("IPP:" #func, CV_TRACE_NS::details::REGION_FLAG_IMPL_IPP)

if(func)

{

CV_IMPL_ADD(CV_IMPL_IPP);

}

else

{

setIppErrorStatus();

CV_Error(cv::Error::StsAssert, #func);

}

return __VA_ARGS__;

}

#else

#define CV_IPP_RUN_(condition, func, ...)

if (cv::ipp::useIPP() && (condition))

{

CV__TRACE_REGION_("IPP:" #func, CV_TRACE_NS::details::REGION_FLAG_IMPL_IPP)

if(func)

{

CV_IMPL_ADD(CV_IMPL_IPP);

return __VA_ARGS__;

}

#endif

#else

#define CV_IPP_RUN_(condition, func, ...)

#endif

#define CV_IPP_RUN_FAST(func, ...) CV_IPP_RUN_(true, func, __VA_ARGS__)

#define CV_IPP_RUN(condition, func, ...) CV_IPP_RUN_((condition), (func), __VA_ARGS__)

x、写个测试用例，请重点关注函数cv::getBuildInformation()

#include

#define IMAGE_PATHNAME "D:allikeImage_"

void checkBuild()

{

//查询opencv编译时配置

std::cout << cv::getBuildInformation() << std::endl;

}

void checkIPP()

{

bool use = cv::ipp::useIPP();

bool useNE = cv::ipp::useIPP_NotExact();

int status = cv::ipp::getIppStatus();

cv::String ver = cv::ipp::getIppVersion();

cv::String loc = cv::ipp::getIppErrorLocation();

std::cout << "IPP use:" << use << std::endl;

std::cout << "IPP useNE:" << useNE << std::endl;

std::cout << "IPP status:" << status << std::endl;

std::cout << "IPP ver:" << ver << std::endl;

std::cout << "IPP loc:" << loc << std::endl;

}

void checkSimd()

{

//查询opencv线程

int numTh = cv::getNumThreads(); //默认值是cpu的逻辑线程数

int numCore = cv::getNumberOfCPUs();

std::cout << "getNumThreads=" << numTh << std::endl;

std::cout << "getNumberOfCPUs=" << numCore << std::endl;

//查询opencv当前是否开启了并行优化功能

bool opt = cv::useOptimized(); //默认值是true

std::cout << "useOptimized=" << opt << std::endl;

//查询opencv当前是否开启了OpenCL功能

bool ret1 = cv::ocl::haveOpenCL();

bool ret2 = cv::ocl::useOpenCL();

std::cout << "default haveOpenCL:" << ret1 << std::endl;

std::cout << "default useOpenCL:" << ret2 << std::endl;

//查询opencv当前是否支持具体的CPU指令集

bool check1 = cv::checkHardwareSupport(CV_CPU_SSE4_1);

bool check2 = cv::checkHardwareSupport(CV_CPU_SSE4_2);

bool check3 = cv::checkHardwareSupport(CV_CPU_AVX2);

std::cout << "CV_CPU_SSE4_1=" << check1 << std::endl;

std::cout << "CV_CPU_SSE4_2=" << check2 << std::endl;

std::cout << "CV_CPU_AVX2=" << check3 << std::endl;

//查询完整的硬件支持清单

std::cout << "HardwareSupport:" << std::endl;

std::cout << "CV_CPU_MMX: " << cv::checkHardwareSupport(CV_CPU_MMX) << std::endl;

std::cout << "CV_CPU_SSE: " << cv::checkHardwareSupport(CV_CPU_SSE) << std::endl;

std::cout << "CV_CPU_SSE2: " << cv::checkHardwareSupport(CV_CPU_SSE2) << std::endl;

std::cout << "CV_CPU_SSE3: " << cv::checkHardwareSupport(CV_CPU_SSE3) << std::endl;

std::cout << "CV_CPU_SSSE3: " << cv::checkHardwareSupport(CV_CPU_SSSE3) << std::endl;

std::cout << "CV_CPU_SSE4_1: " << cv::checkHardwareSupport(CV_CPU_SSE4_1) << std::endl;

std::cout << "CV_CPU_SSE4_2: " << cv::checkHardwareSupport(CV_CPU_SSE4_2) << std::endl;

std::cout << "CV_CPU_POPCNT: " << cv::checkHardwareSupport(CV_CPU_POPCNT) << std::endl;

std::cout << "CV_CPU_FP16: " << cv::checkHardwareSupport(CV_CPU_FP16) << std::endl;

std::cout << "CV_CPU_AVX: " << cv::checkHardwareSupport(CV_CPU_AVX) << std::endl;

std::cout << "CV_CPU_AVX2: " << cv::checkHardwareSupport(CV_CPU_AVX2) << std::endl;

std::cout << "CV_CPU_FMA3: " << cv::checkHardwareSupport(CV_CPU_FMA3) << std::endl;

std::cout << "CV_CPU_AVX_512F: " << cv::checkHardwareSupport(CV_CPU_AVX_512F) << std::endl;

std::cout << "CV_CPU_AVX_512BW: " << cv::checkHardwareSupport(CV_CPU_AVX_512BW) << std::endl;

std::cout << "CV_CPU_AVX_512CD: " << cv::checkHardwareSupport(CV_CPU_AVX_512CD) << std::endl;

std::cout << "CV_CPU_AVX_512DQ: " << cv::checkHardwareSupport(CV_CPU_AVX_512DQ) << std::endl;

std::cout << "CV_CPU_AVX_512ER: " << cv::checkHardwareSupport(CV_CPU_AVX_512ER) << std::endl;

std::cout << "CV_CPU_AVX_512IFMA512: " << cv::checkHardwareSupport(CV_CPU_AVX_512IFMA512) << std::endl;

std::cout << "CV_CPU_AVX_512IFMA: " << cv::checkHardwareSupport(CV_CPU_AVX_512IFMA) << std::endl;

std::cout << "CV_CPU_AVX_512PF: " << cv::checkHardwareSupport(CV_CPU_AVX_512PF) << std::endl;

std::cout << "CV_CPU_AVX_512VBMI: " << cv::checkHardwareSupport(CV_CPU_AVX_512VBMI) << std::endl;

std::cout << "CV_CPU_AVX_512VL: " << cv::checkHardwareSupport(CV_CPU_AVX_512VL) << std::endl;

std::cout << "CV_CPU_NEON: " << cv::checkHardwareSupport(CV_CPU_NEON) << std::endl;

std::cout << "CV_CPU_VSX: " << cv::checkHardwareSupport(CV_CPU_VSX) << std::endl;

std::cout << "CV_CPU_AVX512_SKX: " << cv::checkHardwareSupport(CV_CPU_AVX512_SKX) << std::endl;

std::cout << "CV_HARDWARE_MAX_FEATURE: " << cv::checkHardwareSupport(CV_HARDWARE_MAX_FEATURE) << std::endl;

std::cout << std::endl;

//cv::setUseOptimized(false);

//cv::setNumThreads(1);

}

void checkCuda() //旧版本的是cv::gpu,#include ,已弃用

{

int64 begintime, endtime;

int num_devices = cv::cuda::getCudaEnabledDeviceCount();

if (num_devices <= 0)

{

std::cerr << "There is no cuda device" << std::endl;

return;

}

int enable_device_id = -1;

for (int i = 0; i < num_devices; i++)

{

cv::cuda::DeviceInfo dev_info(i);

if (dev_atible())

{

enable_device_id = i;

}

if (enable_device_id < 0)

{

std::cerr << "GPU module isn't built for GPU" << std::endl;

return;

}

cv::cuda::setDevice(enable_device_id); //指定显卡

//有一个问题是，一般使用GPU加速的话，第一次调用GPU，会很慢很慢，一条简单的语句都用了10多秒左右。

//治标不治本的解决方法是在程序的开头加上一句cv::gpu::GpuMata(10, 10, CV_8U);

//这样会令耗时的操作放在一开头，不那么影响后面的操作时间

cv::cuda::GpuMat(10, 10, CV_8U);

//测试用例

cv::Mat src_image = cv::imread(IMAGE_PATHNAME);

cv::Mat dst_image;

cv::cuda::GpuMat d_src_img(src_image); //upload src image to gpu

cv::cuda::GpuMat d_dst_img;

begintime = cv::getTickCount();

cv::cvtColor(d_src_img, d_dst_img, cv::COLOR_BGR2GRAY); //canny

d_dst_ad(dst_image); //download dst image to cpu

endtime = cv::getTickCount();

std::cerr << 1000 * (endtime - begintime) / cv::getTickFrequency() << std::endl;

cv::imshow("test", dst_image);

cv::waitKey(0);

}

void checkOpenCL() //Open Computing Language:开放计算语言,可以附加在主机处理器的CPU或GPU上执行

{

std::vector info;

cv::ocl::getPlatfomsInfo(info);

cv::ocl::PlatformInfo sdk = (0);

int number = Number();

if (number < 1)

{

std::cout << "Number of devices:" << number << std::endl; std::cout << "Number of devices:" << number << std::endl; return; } std::cout << "***********SDK************" << std::endl; std::cout << "Name:" << () << std::endl; std::cout << "Vendor:" << () << std::endl; std::cout << "Version:" << n() << std::endl; std::cout << "Version:" << n() << std::endl; std::cout << "Number of devices:" << number << std::endl; for (int i = 0; i < number; i++) { std::cout << std::endl; cv::ocl::Device device; ice(device, i); std::cout << "***********Device " << i + 1 << "***********" << std::endl; std::cout << "Vendor Id:" << ID() << std::endl; std::cout << "Vendor name:" << Name() << std::endl; std::cout << "Name:" << () << std::endl; std::cout << "Driver version:" << ID() << std::endl; if (()) std::cout << "Is AMD device" << std::endl; if (l()) std::cout << "Is Intel device" << std::endl; if (ia()) std::cout << "Is NVidia device" << std::endl; std::cout << "Global Memory size:" << MemSize() << std::endl; std::cout << "Memory cache size:" << MemCacheSize() << std::endl; std::cout << "Memory cache type:" << MemCacheType() << std::endl; std::cout << "Local Memory size:" << emSize() << std::endl; std::cout << "Local Memory type:" << emType() << std::endl; std::cout << "Max Clock frequency:" << ckFrequency() << std::endl; }}void calcEdgesCPU(){ cv::ocl::setUseOpenCL(false); bool ret1 = cv::ocl::haveOpenCL(); bool ret2 = cv::ocl::useOpenCL(); std::cout << "haveOpenCL:" << ret1 << std::endl; std::cout << "useOpenCL:" << ret2 << std::endl; double start = cv::getTickCount(); cv::Mat cpuGray, cpuBlur, cpuEdges; cv::Mat cpuFrame = cv::imread(IMAGE_PATHNAME); cvtColor(cpuFrame, cpuGray, cv::COLOR_BGR2GRAY); cv::GaussianBlur(cpuGray, cpuBlur, cv::Size(3, 3), 15, 15); cv::Canny(cpuBlur, cpuEdges, 50, 100, 3); std::vector cir; //cv::HoughCircles(cpuBlur, cir, cv::HOUGH_GRADIENT_ALT, 1.5, 15, 300, 0.8, 1, 100); //cv::HoughCircles(cpuBlur, cir, cv::HOUGH_GRADIENT, 1, 15, 100, 30, 1, 100); std::cout << "CPU cost time:(s)" << ((cv::getTickCount() - start) / cv::getTickFrequency()) << std::endl; cv::namedWindow("Canny Edges CPU", cv::WINDOW_NORMAL); cv::imshow("Canny Edges CPU", cpuEdges);

cv::imshow("Canny Edges CPU", cpuEdges);

}

void calcEdgesGPU()

{

cv::ocl::setUseOpenCL(true);

bool ret1 = cv::ocl::haveOpenCL();

bool ret2 = cv::ocl::useOpenCL();

std::cout << "haveOpenCL:" << ret1 << std::endl;

std::cout << "useOpenCL:" << ret2 << std::endl;

//通过使用UMat对象，OpenCV会自动在支持OpenCL的设备上使用GPU运算，在不支持OpenCL的设备仍然使用CPU运算，这样就避免了程序运行失败，而且统一

double start = cv::getTickCount();

cv::UMat gpuFrame, gpuGray, gpuBlur, gpuEdges;

cv::Mat cpuFrame = cv::imread(IMAGE_PATHNAME);

(gpuFrame); //Mat与UMat相互转换

cvtColor(gpuFrame, gpuGray, cv::COLOR_BGR2GRAY);

cv::GaussianBlur(gpuGray, gpuBlur, cv::Size(3, 3), 15, 15);

cv::Canny(gpuBlur, gpuEdges, 50, 100, 3);

std::vector cir;

//cv::HoughCircles(gpuBlur, cir, cv::HOUGH_GRADIENT_ALT, 1.5, 15, 300, 0.8, 1, 100);

//cv::HoughCircles(gpuBlur, cir, cv::HOUGH_GRADIENT, 1, 15, 100, 30, 1, 100);

std::cout << "GPU cost time:(s)" << ((cv::getTickCount() - start) / cv::getTickFrequency()) << std::endl;

cv::Mat matResult = (cv::ACCESS_READ); //Mat与UMat相互转换

cv::namedWindow("Canny Edges GPU1", cv::WINDOW_NORMAL);

cv::imshow("Canny Edges GPU1", matResult);

cv::namedWindow("Canny Edges GPU2", cv::WINDOW_NORMAL);

cv::imshow("Canny Edges GPU2", gpuEdges);

}

int main(int argc, char *argv[])

{

checkBuild();

checkIPP();

checkSimd();

checkCuda();

checkOpenCL();

calcEdgesCPU();

calcEdgesGPU();

cv::waitKey(0);

return 0;

}

cv::getBuildInformation()得到的结果是：

General configuration for OpenCV 4.5.5 =====================================

Version control: 4.5.5

Platform:

Timestamp: 2021-12-25T04:33:15Z

Host: Windows 10.0.19041 AMD64

CMake: 3.16.4

CMake generator: Visual Studio 15 2017

CMake build tool: C:/Program Files (x86)/Microsoft Visual Studio/2017/Professional/MSBuild/15.0/Bin/

MSVC: 1916

Configuration: Debug Release

CPU/HW features:

Baseline: SSE SSE2 SSE3 Baseline: SSE SSE2 SSE3 requested: SSE3 Dispatched code generation: SSE4_1 SSE4_2 FP16 AVX AVX2 AVX512_SKX requested: SSE4_1 SSE4_2 AVX FP16 AVX2 AVX512_SKX SSE4_1 (16 files): + SSSE3 SSE4_1 SSE4_2 (1 files): + SSSE3 SSE4_1 POPCNT SSE4_2 FP16 (0 files): + SSSE3 SSE4_1 POPCNT SSE4_2 FP16 AVX AVX (4 files): + SSSE3 SSE4_1 POPCNT SSE4_2 AVX AVX2 (31 files): + SSSE3 SSE4_1 POPCNT SSE4_2 FP16 FMA3 AVX AVX2 AVX512_SKX (5 files): + SSSE3 SSE4_1 POPCNT SSE4_2 FP16 FMA3 AVX AVX2 AVX_512F AVX512_COMMON AVX512_SKX C/C++: Built as dynamic libs?: YES C++ standard: 11 C++ Compiler: C:/Program Files (x86)/Microsoft Visual Studio/2017/Professional/VC/Tools/MSVC/14.16.27023/bin/Hostx86/x64/ (ver 19.16.270 C++ flags (Release): /DWIN32 /D_WINDOWS /W4 /GR /D _CRT_SECURE_NO_DEPRECATE /D _CRT_NONSTDC_NO_DEPRECATE /D _SCL_SECURE C Compiler: C:/Program Files (x86)/Microsoft Visual Studio/2017/Professional/VC/Tools/MSVC/14.16.27023/bin/Hostx86/x64/ C++ flags (Debug): /DWIN32 /D_WINDOWS /W4 /GR /D _CRT_SECURE_NO_DEPRECATE /D _CRT_NONSTDC_NO_DEPRECATE /D _SCL_SECURE C flags (Release): /DWIN32 /D_WINDOWS /W3 /D _CRT_SECURE_NO_DEPRECATE /D _CRT_NONSTDC_NO_DEPRECATE /D _SCL_SECURE_NO_ Linker flags (Release): /machine:x64 /INCREMENTAL:NO Linker flags (Debug): /machine:x64 /debug /INCREMENTAL ccache: NO Precompiled headers: NO Extra dependencies: 3rdparty dependencies: OpenCV modules: To be built: calib3d core dnn features2d flann gapi highgui imgcodecs imgproc ml objdetect photo stitching video videoio world Disabled: python2 python3 Disabled by dependency: - Unavailable: java ts Applications: apps Documentation: NO Non-free algorithms: NO Windows RT support: NO GUI: Win32 UI: YES VTK support: NO Media I/O: ZLib: build (ver 1.2.11) JPEG: build-libjpeg-turbo (ver 2.1.2-62) WEBP: build (ver encoder: 0x020f) PNG: build (ver 1.6.37) TIFF: build (ver 42 - 4.2.0) JPEG 2000: build (ver 2.4.0) OpenEXR: build (ver 2.3.0) HDR: YES SUNRASTER: YES PXM: YES PFM: YES Video I/O: DC1394: NO FFMPEG: YES (prebuilt binaries) avcodec: YES (58.134.100) avformat: YES (58.76.100) avutil: YES (56.70.100) swscale: YES (5.9.100) avresample: YES (4.0.0) GStreamer: NO DirectShow: YES Media Foundation: YES C flags (Debug): /DWIN32 /D_WINDOWS /W3 /D _CRT_SECURE_NO_DEPRECATE /D _CRT_NONSTDC_NO_DEPRECATE /D _SCL_SECURE_NO_ Media Foundation: YES DXVA: YES Parallel framework: Concurrency Trace: YES (with Intel ITT) Other third-party libraries: Intel IPP: 2020.0.0 Gold [2020.0.0] at: C:/build/master_winpack-build-win64-vc15/build/3rdparty/ippicv/ippicv_win/icv Intel IPP IW: sources (2020.0.0) at: C:/build/master_winpack-build-win64-vc15/build/3rdparty/ippicv/ippicv_win/iw Eigen: NO Custom HAL: NO Protobuf: build (3.19.1) OpenCL: YES (NVD3D11) Include path: C:/build/master_winpack-build-win64-vc15/opencv/3rdparty/include/opencl/1.2 Link libraries: Dynamic load Python (for build): C:/utils/soft/python27-x64/ Java: ant: C:/utils/soft/apache-ant-1.9.7/bin/ (ver 1.9.7) Java wrappers: NO Java tests: NO Install to: C:/build/master_winpack-build-win64-vc15/install----------------------------------------------------------------- JNI: C:/Program Files/Java/jdk1.8.0_112/include C:/Program Files/Java/jdk1.8.0_112/include/win32 C:/Program Files/Java/jdk1.8.0_112/include

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OpenCV算法加速(4)官方源码v4.5.5的默认并行和优化加速的编译选项是什

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