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

 1 #include <iostream>
 2 #include <opencv2/core/ocl.hpp>
 3 #include <opencv2/opencv.hpp>
 4  
 5 using namespace std;
 6 using namespace cv;
 7 using namespace cv::ocl;
 8  
 9 int main()
10 {
11     vector<ocl::PlatformInfo> info;
12     getPlatfomsInfo(info);
13     PlatformInfo sdk = info.at(0);
14  
15     if (sdk.deviceNumber() < 1)
16         return -1;
17  
18     cout << "***********SDK************" << endl;
19     cout << "Name:" << sdk.name() << endl;
20     cout << "Vendor:" << sdk.vendor() << endl;
21     cout << "Version:" << sdk.version() << endl;
22     cout << "Version:" << sdk.version() << endl;
23     cout << "Number of devices:" << sdk.deviceNumber() << endl;
24  
25     for (int i = 0; i < sdk.deviceNumber(); i++) {
26         cout << endl;
27         Device device;
28         sdk.getDevice(device, i);
29         cout << "************* Device " << i + 1 << endl;
30  
31         cout << "Vendor Id:" << device.vendorID() << endl;
32         cout << "Vendor name:" << device.vendorName() << endl;
33         cout << "Name:" << device.name() << endl;
34         cout << "Driver version:" << device.vendorID() << endl;
35         if (device.isAMD()) cout << "Is AMD device" << endl;
36         if (device.isIntel()) cout << "Is Intel device" << endl;
37         if (device.isNVidia()) cout << "Is NVidia device" << endl;
38  
39         cout << "Global Memory size:" << device.globalMemSize() << endl;
40         cout << "Memory cache size:" << device.globalMemCacheSize() << endl;
41         cout << "Memory cache type:" << device.globalMemCacheType() << endl;
42         cout << "Local Memory size:" << device.localMemSize() << endl;
43         cout << "Local Memory type:" << device.localMemType() << endl;
44         cout << "Max Clock frequency:" << device.maxClockFrequency() << endl;
45     }
46     getchar();
47  
48     return 0;
49 }

CPU和GPU处理对比

 1 #include <iostream>
 2 #include <opencv2/core/ocl.hpp>
 3 #include <opencv2/opencv.hpp>
 4  
 5 using namespace std;
 6 using namespace cv;
 7 using namespace cv::ocl;
 8  
 9 void calEdgesCPU(void);
10 void calEdgesGPU(void);
11  
12 int main()
13 {
14     calEdgesCPU();
15     calEdgesGPU();
16     getchar();
17  
18     return 0;
19 }
20  
21 void calEdgesCPU() {
22     double start=getTickCount();
23     Mat cpuBw, cpuBlur, cpuEdges;
24     Mat cpuFrame = imread("test.jpg");
25     //namedWindow("Canny Edges CPU", 1);
26     cvtColor(cpuFrame, cpuBw, COLOR_BGR2GRAY);
27     GaussianBlur(cpuBw, cpuBlur, Size(1, 1), 1.5, 1.5);
28     Canny(cpuBlur, cpuEdges, 50, 100, 3);
29     //imshow("Canny Edges CPU", cpuEdges);
30     cout << "CPU cost time:" << ((getTickCount() - start) / getTickFrequency()) << endl;
31 }
32  
33 void calEdgesGPU() {
34     setUseOpenCL(true);
35     double start = getTickCount();
36     UMat gpuBw, gpuBlur, gpuEdges,gpuFrame;
37     Mat cpuFrame = imread("test.jpg");
38     cpuFrame.copyTo(gpuFrame);
39  
40     //namedWindow("Canny Edges GPU", 1);
41     cvtColor(gpuFrame, gpuBw, COLOR_BGR2GRAY);
42     GaussianBlur(gpuBw, gpuBlur, Size(1, 1), 1.5, 1.5);
43     Canny(gpuBlur, gpuEdges, 50, 100, 3);
44     //imshow("Canny Edges CPU", gpuEdges);
45     cout << "GPU cost time:" << ((getTickCount() - start) / getTickFrequency()) << endl;
46 }

人脸辨认、GPU和CPU处理区别

 1 #include <iostream>
 2 #include <opencv2/core/core.hpp>
 3 #include <opencv2/core/ocl.hpp>
 4 #include <opencv2/objdetect.hpp>
 5 #include <opencv2/videoio.hpp>
 6 #include <opencv2/highgui.hpp>
 7 #include <opencv2/imgproc.hpp>
 8  
 9  
10 using namespace std;
11 using namespace cv;
12 using namespace cv::ocl;
13  
14 int main()
15 {
16     //1-设置初始参数
17     //用来存储人脸的向量
18     vector<Rect> faces;
19     CascadeClassifier face_cascade;
20     String face_cascade_name = "D:\OpenCV\opencv\sources\data\haarcascades_cuda\haarcascade_frontalface_alt.xml";
21     int face_size = 30;
22     double scale_factor = 1.1;
23     int min_neighbours = 2;
24  
25     VideoCapture cap(0);
26     UMat frame, frameGray;
27     bool finish = false;
28  
29     //2-加载xml文件，以使用分类器
30     if (!face_cascade.load(face_cascade_name)) {
31         cout << "Cannot load the face xml" << endl;
32         return -1;
33     }
34     namedWindow("Video Capture");
35  
36     //3-选择用CPU处理还是GPU处理
37     bool cpu_gpu = false;
38     setUseOpenCL(cpu_gpu);
39  
40     Rect r;
41     double start_time, finish_time, start_total_time, finish_total_time;
42     int counter = 0;
43  
44     //4-为每幅拍摄图像检测人脸
45     start_total_time = getTickCount();
46     while (!finish) {
47         start_time = getTickCount();
48         cap >> frame;
49         if (frame.empty()) {
50             cout << "No capture frame" << endl;
51             break;
52         }
53         cvtColor(frame, frameGray, COLOR_BGR2GRAY);
54         equalizeHist(frameGray, frameGray);
55         //检测人脸
56         face_cascade.detectMultiScale(frameGray, faces, scale_factor, min_neighbours, 0 | CASCADE_SCALE_IMAGE, Size(face_size, face_size));
57         //对每个检测到的人脸
58         for (int f = 0; f < faces.size(); f++) {
59             r = faces[f];
60             //在人脸上画框
61             rectangle(frame, Point(r.x, r.y), Point(r.x + r.width, r.y + r.height), Scalar(0, 255, 0), 3);
62         }
63         imshow("Video Capture", frame);
64         //计算处理时间
65         finish_time = getTickCount();
66         //cout << "Time per frame:" << (finish_time - start_time) / getTickFrequency() << "sec" << endl;
67         counter++;
68             //按下Esc结束
69             if (waitKey(1) == 27) finish = true;
70             
71     }
72     finish_total_time = getTickCount();
73     cout << "Average time per frame:" << ((finish_total_time - start_total_time) / getTickFrequency() / counter) <<"sec"<< endl;
74  
75     getchar();
76  
77     return 0;
78 }

GPU平均时间：

CPU平均时间：