OPENCV例子opencv

OPENCV例子opencv

该程序演示了使用广义霍夫变换进行任意对象查找，仅检测位置，无需平移和旋转。

相关类的继承关系如下图：

示例的调用关系如下图：

 

main的调用关系如下图:

 

main的流程图如下图:

 

main的UML逻辑图如下图:

 

示例源代码：

#include <vector>

#include <iostream>

#include <string>

#include "opencv2/core.hpp"

#include "opencv2/core/utility.hpp"

#include "opencv2/imgproc.hpp"

#include "opencv2/cudaimgproc.hpp"

#include "opencv2/highgui.hpp"

using namespace std;

using namespace cv;

static Mat loadImage(const string& name)

{

    Mat image = imread(name, IMREAD_GRAYSCALE);

    if (image.empty())

    {

        cerr << "Can't load image - " << name << endl;//无法载入图片

        exit(-1);

    }

    return image;

}

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

{

    CommandLineParser cmd(argc, argv,

        "{ image i        | ../data/pic1.png  | input image }"           //图片i

        "{ template t     | templ.png | template image }"                //模板        

        "{ full           |           | estimate scale and rotation }"        //估计尺度和旋转        

        "{ gpu            |           | use gpu version }"        //使用GPU

        "{ minDist        | 100       | minimum distance between the centers of the detected objects }"//最小的距离（被检测物体的中心之间）

        "{ levels         | 360       | R-Table levels }"//RTable的层级

        "{ votesThreshold | 30        | the accumulator threshold for the template centers at the detection stage. The smaller it is, the more false positions may be detected }"//检测阶段模板中心的累加器阈值。它越小，可能检测到的错误位置越多

        "{ angleThresh    | 10000     | angle votes threshold }"//角度门槛

        "{ scaleThresh    | 1000      | scale votes threshold }"//尺度门槛

        "{ posThresh      | 100       | position votes threshold }"//位置门槛

        "{ dp             | 2         | inverse ratio of the accumulator resolution to the image resolution }"//累加器分辨率与图像分辨率的反比

        "{ minScale       | 0.5       | minimal scale to detect }"//检测的最小尺度

        "{ maxScale       | 2         | maximal scale to detect }"//检测的最大尺度

        "{ scaleStep      | 0.05      | scale step }"//尺度步长

        "{ minAngle       | 0         | minimal rotation angle to detect in degrees }"//以度为单位检测的最小旋转角度

        "{ maxAngle       | 360       | maximal rotation angle to detect in degrees }"//以度为单位检测的最大旋转角度

        "{ angleStep      | 1         | angle step in degrees }"//角度步长

        "{ maxBufSize     | 1000      | maximal size of inner buffers }"//内部缓冲区的最大大小

        "{ help h ?       |           | print help message }"//打印帮助信息

    );

    cmd.about("This program demonstrates arbitrary object finding with the Generalized Hough transform.");

    if (cmd.has("help"))

    {

        cmd.printMessage();

        return 0;

    }

    const string templName = cmd.get<string>("template");

    const string imageName = cmd.get<string>("image");

    const bool full = cmd.has("full");

    const bool useGpu = cmd.has("gpu");

    const double minDist = cmd.get<double>("minDist");

    const int levels = cmd.get<int>("levels");

    const int votesThreshold = cmd.get<int>("votesThreshold");

    const int angleThresh = cmd.get<int>("angleThresh");

    const int scaleThresh = cmd.get<int>("scaleThresh");

    const int posThresh = cmd.get<int>("posThresh");

    const double dp = cmd.get<double>("dp");

    const double minScale = cmd.get<double>("minScale");

    const double maxScale = cmd.get<double>("maxScale");

    const double scaleStep = cmd.get<double>("scaleStep");

    const double minAngle = cmd.get<double>("minAngle");

    const double maxAngle = cmd.get<double>("maxAngle");

    const double angleStep = cmd.get<double>("angleStep");

    const int maxBufSize = cmd.get<int>("maxBufSize");

    if (!cmd.check())

    {

        cmd.printErrors();

        return -1;

    }

    Mat templ = loadImage(templName);

    Mat image = loadImage(imageName);

    Ptr<GeneralizedHough> alg;

    if (!full)

    {

        Ptr<GeneralizedHoughBallard> ballard = useGpu ? cuda::createGeneralizedHoughBallard() : createGeneralizedHoughBallard();

        ballard->setMinDist(minDist);

        ballard->setLevels(levels);

        ballard->setDp(dp);

        ballard->setMaxBufferSize(maxBufSize);

        ballard->setVotesThreshold(votesThreshold);

        alg = ballard;

    }

    else

    {

        Ptr<GeneralizedHoughGuil> guil = useGpu ? cuda::createGeneralizedHoughGuil() : createGeneralizedHoughGuil();

        guil->setMinDist(minDist);

        guil->setLevels(levels);

        guil->setDp(dp);

        guil->setMaxBufferSize(maxBufSize);

        guil->setMinAngle(minAngle);

        guil->setMaxAngle(maxAngle);

        guil->setAngleStep(angleStep);

        guil->setAngleThresh(angleThresh);

        guil->setMinScale(minScale);

        guil->setMaxScale(maxScale);

        guil->setScaleStep(scaleStep);

        guil->setScaleThresh(scaleThresh);

        guil->setPosThresh(posThresh);

        alg = guil;

    }

    vector<Vec4f> position;

    TickMeter tm;

    if (useGpu)

    {

        cuda::GpuMat d_templ(templ);

        cuda::GpuMat d_image(image);

        cuda::GpuMat d_position;

        alg->setTemplate(d_templ);

        tm.start();

        alg->detect(d_image, d_position);

        d_position.download(position);

        tm.stop();

    }

    else

    {

        alg->setTemplate(templ);

        tm.start();

        alg->detect(image, position);

        tm.stop();

    }

    cout << "Found : " << position.size() << " objects" << endl;

    cout << "Detection time : " << tm.getTimeMilli() << " ms" << endl;

    Mat out;

    cv::cvtColor(image, out, COLOR_GRAY2BGR);

    for (size_t i = 0; i < position.size(); ++i)

    {

        Point2f pos(position[i][0], position[i][1]);

        float scale = position[i][2];

        float angle = position[i][3];

        RotatedRect rect;

        rect.center = pos;

        rect.size = Size2f(templ.cols * scale, templ.rows * scale);

        rect.angle = angle;

        Point2f pts[4];

        rect.points(pts);

        line(out, pts[0], pts[1], Scalar(0, 0, 255), 3);

        line(out, pts[1], pts[2], Scalar(0, 0, 255), 3);

        line(out, pts[2], pts[3], Scalar(0, 0, 255), 3);

        line(out, pts[3], pts[0], Scalar(0, 0, 255), 3);

    }

    imshow("out", out);

    waitKey();

    return 0;

}