OpenCV 加速图像处理

• OpenCV库包括了对OpenCL和CUDA GPU架构的支持。
• OpenCL（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。
• OpenCV有一个新的统一数据结构UMat，用于在必要和可能的时候，负责将数据传输到GPU。
• 目前，有5个可用的OpenCL SDK：AMD APP SDK、Intel SDK、IBM OpenCL开发工具包、IBM OpenCL公共运行库、Nvidia OpenCL驱动程序和工具。

 

检查你的OpenCL是否可用

#include <iostream>
#include <opencv2/core/ocl.hpp>
#include <opencv2/opencv.hpp>
 
using namespace std;
using namespace cv;
using namespace cv::ocl;
 
int main()
{
    vector<ocl::PlatformInfo> info;
    getPlatfomsInfo(info);
    PlatformInfo sdk = info.at(0);
 
    if (sdk.deviceNumber() < 1)
        return -1;
 
    cout << "***********SDK************" << endl;
    cout << "Name:" << sdk.name() << endl;
    cout << "Vendor:" << sdk.vendor() << endl;
    cout << "Version:" << sdk.version() << endl;
    cout << "Version:" << sdk.version() << endl;
    cout << "Number of devices:" << sdk.deviceNumber() << endl;
 
    for (int i = 0; i < sdk.deviceNumber(); i++) {
        cout << endl;
        Device device;
        sdk.getDevice(device, i);
        cout << "************* Device " << i + 1 << endl;
 
        cout << "Vendor Id:" << device.vendorID() << endl;
        cout << "Vendor name:" << device.vendorName() << endl;
        cout << "Name:" << device.name() << endl;
        cout << "Driver version:" << device.vendorID() << endl;
        if (device.isAMD()) cout << "Is AMD device" << endl;
        if (device.isIntel()) cout << "Is Intel device" << endl;
        if (device.isNVidia()) cout << "Is NVidia device" << endl;
 
        cout << "Global Memory size:" << device.globalMemSize() << endl;
        cout << "Memory cache size:" << device.globalMemCacheSize() << endl;
        cout << "Memory cache type:" << device.globalMemCacheType() << endl;
        cout << "Local Memory size:" << device.localMemSize() << endl;
        cout << "Local Memory type:" << device.localMemType() << endl;
        cout << "Max Clock frequency:" << device.maxClockFrequency() << endl;
    }
    getchar();
 
    return 0;
}

 

CPU和GPU处理对比

#include <iostream>
#include <opencv2/core/ocl.hpp>
#include <opencv2/opencv.hpp>
 
using namespace std;
using namespace cv;
using namespace cv::ocl;
 
void calEdgesCPU(void);
void calEdgesGPU(void);
 
int main()
{
    calEdgesCPU();
    calEdgesGPU();
    getchar();
 
    return 0;
}
 
void calEdgesCPU() {
    double start=getTickCount();
    Mat cpuBw, cpuBlur, cpuEdges;
    Mat cpuFrame = imread("test.jpg");
    //namedWindow("Canny Edges CPU", 1);
    cvtColor(cpuFrame, cpuBw, COLOR_BGR2GRAY);
    GaussianBlur(cpuBw, cpuBlur, Size(1, 1), 1.5, 1.5);
    Canny(cpuBlur, cpuEdges, 50, 100, 3);
    //imshow("Canny Edges CPU", cpuEdges);
    cout << "CPU cost time:" << ((getTickCount() - start) / getTickFrequency()) << endl;
}
 
void calEdgesGPU() {
    setUseOpenCL(true);
    double start = getTickCount();
    UMat gpuBw, gpuBlur, gpuEdges,gpuFrame;
    Mat cpuFrame = imread("test.jpg");
    cpuFrame.copyTo(gpuFrame);
 
    //namedWindow("Canny Edges GPU", 1);
    cvtColor(gpuFrame, gpuBw, COLOR_BGR2GRAY);
    GaussianBlur(gpuBw, gpuBlur, Size(1, 1), 1.5, 1.5);
    Canny(gpuBlur, gpuEdges, 50, 100, 3);
    //imshow("Canny Edges CPU", gpuEdges);
    cout << "GPU cost time:" << ((getTickCount() - start) / getTickFrequency()) << endl;
}

 

人脸辨认、GPU和CPU处理区别

#include <iostream>
#include <opencv2/core/core.hpp>
#include <opencv2/core/ocl.hpp>
#include <opencv2/objdetect.hpp>
#include <opencv2/videoio.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/imgproc.hpp>
 
 
using namespace std;
using namespace cv;
using namespace cv::ocl;
 
int main()
{
    //1-设置初始参数
    //用来存储人脸的向量
    vector<Rect> faces;
    CascadeClassifier face_cascade;
    String face_cascade_name = "D:\OpenCV\opencv\sources\data\haarcascades_cuda\haarcascade_frontalface_alt.xml";
    int face_size = 30;
    double scale_factor = 1.1;
    int min_neighbours = 2;
 
    VideoCapture cap(0);
    UMat frame, frameGray;
    bool finish = false;
 
    //2-加载xml文件，以使用分类器
    if (!face_cascade.load(face_cascade_name)) {
        cout << "Cannot load the face xml" << endl;
        return -1;
    }
    namedWindow("Video Capture");
 
    //3-选择用CPU处理还是GPU处理
    bool cpu_gpu = false;
    setUseOpenCL(cpu_gpu);
 
    Rect r;
    double start_time, finish_time, start_total_time, finish_total_time;
    int counter = 0;
 
    //4-为每幅拍摄图像检测人脸
    start_total_time = getTickCount();
    while (!finish) {
        start_time = getTickCount();
        cap >> frame;
        if (frame.empty()) {
            cout << "No capture frame" << endl;
            break;
        }
        cvtColor(frame, frameGray, COLOR_BGR2GRAY);
        equalizeHist(frameGray, frameGray);
        //检测人脸
        face_cascade.detectMultiScale(frameGray, faces, scale_factor, min_neighbours, 0 | CASCADE_SCALE_IMAGE, Size(face_size, face_size));
        //对每个检测到的人脸
        for (int f = 0; f < faces.size(); f++) {
            r = faces[f];
            //在人脸上画框
            rectangle(frame, Point(r.x, r.y), Point(r.x + r.width, r.y + r.height), Scalar(0, 255, 0), 3);
        }
        imshow("Video Capture", frame);
        //计算处理时间
        finish_time = getTickCount();
        //cout << "Time per frame:" << (finish_time - start_time) / getTickFrequency() << "sec" << endl;
        counter++;
            //按下Esc结束
            if (waitKey(1) == 27) finish = true;
            
    }
    finish_total_time = getTickCount();
    cout << "Average time per frame:" << ((finish_total_time - start_total_time) / getTickFrequency() / counter) <<"sec"<< endl;
 
    getchar();
 
    return 0;
}

GPU平均时间：

CPU平均时间：