C++ 使用OProfile分析OpenCV

我有一个基本的OpenCV程序：

#include <iostream>
#include "opencv2/opencv.hpp"

int main(){

    std::cout<<"Reading Image..."<<std::endl;
    cv::Mat img = cv::imread("all_souls_000000.jpg", cv::IMREAD_GRAYSCALE);
    if(!img.data)
        std::cerr<<"Error reading image"<<std::endl;
    return 0;
}

返回：

Kernel profiling is not possible with current system config.
Set /proc/sys/kernel/kptr_restrict to 0 to collect kernel samples.
operf: Profiler started
* * * * WARNING: Profiling rate was throttled back by the kernel * * * *
The number of samples actually recorded is less than expected, but is
probably still statistically valid.  Decreasing the sampling rate is the
best option if you want to avoid throttling.

Profiling done.

---

为什么会发生这种情况？评测OpenCV的最佳方法是什么？

我能够在OpenCV应用程序上运行

operf

，有了这个结果，这就是你想要的吗

Profiling started at Tue Jan 31 16:52:48 2017
Profiling stopped at Tue Jan 31 16:52:53 2017

-- OProfile/operf Statistics --
Nr. non-backtrace samples: 337018
Nr. kernel samples: 5603
Nr. user space samples: 331415
Nr. samples lost due to sample address not in expected range for domain: 0
Nr. lost kernel samples: 0
Nr. samples lost due to sample file open failure: 0
Nr. samples lost due to no permanent mapping: 0
Nr. user context kernel samples lost due to no app info available: 0
Nr. user samples lost due to no app info available: 0
Nr. backtraces skipped due to no file mapping: 0
Nr. hypervisor samples dropped due to address out-of-range: 0
Nr. samples lost reported by perf_events kernel: 0

---

不，我需要知道在每个调用函数上花费的百分比（以及可能花费的时间），这样我就可以找到最昂贵的函数并尝试将它们并行化

Profiling started at Tue Jan 31 16:52:48 2017
Profiling stopped at Tue Jan 31 16:52:53 2017

-- OProfile/operf Statistics --
Nr. non-backtrace samples: 337018
Nr. kernel samples: 5603
Nr. user space samples: 331415
Nr. samples lost due to sample address not in expected range for domain: 0
Nr. lost kernel samples: 0
Nr. samples lost due to sample file open failure: 0
Nr. samples lost due to no permanent mapping: 0
Nr. user context kernel samples lost due to no app info available: 0
Nr. user samples lost due to no app info available: 0
Nr. backtraces skipped due to no file mapping: 0
Nr. hypervisor samples dropped due to address out-of-range: 0
Nr. samples lost reported by perf_events kernel: 0