feat: 切换后端至PaddleOCR-NCNN，切换工程为CMake
1.项目后端整体迁移至PaddleOCR-NCNN算法，已通过基本的兼容性测试 2.工程改为使用CMake组织，后续为了更好地兼容第三方库，不再提供QMake工程 3.重整权利声明文件，重整代码工程，确保最小化侵权风险 Log: 切换后端至PaddleOCR-NCNN，切换工程为CMake Change-Id: I4d5d2c5d37505a4a24b389b1a4c5d12f17bfa38c
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wangzhengyang
182
3rdparty/opencv-4.5.4/samples/gpu/generalized_hough.cpp
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3rdparty/opencv-4.5.4/samples/gpu/generalized_hough.cpp
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#include <vector>
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#include <iostream>
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#include <string>
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#include "opencv2/core.hpp"
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#include "opencv2/core/utility.hpp"
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#include "opencv2/imgproc.hpp"
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#include "opencv2/cudaimgproc.hpp"
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#include "opencv2/highgui.hpp"
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using namespace std;
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using namespace cv;
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static Mat loadImage(const string& name)
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{
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Mat image = imread(name, IMREAD_GRAYSCALE);
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if (image.empty())
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{
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cerr << "Can't load image - " << name << endl;
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exit(-1);
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}
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return image;
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}
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int main(int argc, const char* argv[])
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{
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CommandLineParser cmd(argc, argv,
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"{ image i | ../data/pic1.png | input image }"
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"{ template t | templ.png | template image }"
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"{ full | | estimate scale and rotation }"
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"{ gpu | | use gpu version }"
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"{ minDist | 100 | minimum distance between the centers of the detected objects }"
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"{ levels | 360 | R-Table levels }"
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"{ votesThreshold | 30 | the accumulator threshold for the template centers at the detection stage. The smaller it is, the more false positions may be detected }"
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"{ angleThresh | 10000 | angle votes threshold }"
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"{ scaleThresh | 1000 | scale votes threshold }"
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"{ posThresh | 100 | position votes threshold }"
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"{ dp | 2 | inverse ratio of the accumulator resolution to the image resolution }"
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"{ minScale | 0.5 | minimal scale to detect }"
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"{ maxScale | 2 | maximal scale to detect }"
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"{ scaleStep | 0.05 | scale step }"
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"{ minAngle | 0 | minimal rotation angle to detect in degrees }"
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"{ maxAngle | 360 | maximal rotation angle to detect in degrees }"
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"{ angleStep | 1 | angle step in degrees }"
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"{ maxBufSize | 1000 | maximal size of inner buffers }"
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"{ help h ? | | print help message }"
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);
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cmd.about("This program demonstrates arbitrary object finding with the Generalized Hough transform.");
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if (cmd.has("help"))
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{
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cmd.printMessage();
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return 0;
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}
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const string templName = cmd.get<string>("template");
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const string imageName = cmd.get<string>("image");
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const bool full = cmd.has("full");
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const bool useGpu = cmd.has("gpu");
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const double minDist = cmd.get<double>("minDist");
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const int levels = cmd.get<int>("levels");
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const int votesThreshold = cmd.get<int>("votesThreshold");
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const int angleThresh = cmd.get<int>("angleThresh");
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const int scaleThresh = cmd.get<int>("scaleThresh");
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const int posThresh = cmd.get<int>("posThresh");
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const double dp = cmd.get<double>("dp");
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const double minScale = cmd.get<double>("minScale");
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const double maxScale = cmd.get<double>("maxScale");
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const double scaleStep = cmd.get<double>("scaleStep");
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const double minAngle = cmd.get<double>("minAngle");
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const double maxAngle = cmd.get<double>("maxAngle");
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const double angleStep = cmd.get<double>("angleStep");
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const int maxBufSize = cmd.get<int>("maxBufSize");
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if (!cmd.check())
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{
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cmd.printErrors();
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return -1;
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}
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Mat templ = loadImage(templName);
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Mat image = loadImage(imageName);
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Ptr<GeneralizedHough> alg;
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if (!full)
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{
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Ptr<GeneralizedHoughBallard> ballard = useGpu ? cuda::createGeneralizedHoughBallard() : createGeneralizedHoughBallard();
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ballard->setMinDist(minDist);
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ballard->setLevels(levels);
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ballard->setDp(dp);
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ballard->setMaxBufferSize(maxBufSize);
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ballard->setVotesThreshold(votesThreshold);
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alg = ballard;
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}
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else
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{
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Ptr<GeneralizedHoughGuil> guil = useGpu ? cuda::createGeneralizedHoughGuil() : createGeneralizedHoughGuil();
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guil->setMinDist(minDist);
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guil->setLevels(levels);
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guil->setDp(dp);
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guil->setMaxBufferSize(maxBufSize);
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guil->setMinAngle(minAngle);
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guil->setMaxAngle(maxAngle);
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guil->setAngleStep(angleStep);
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guil->setAngleThresh(angleThresh);
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guil->setMinScale(minScale);
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guil->setMaxScale(maxScale);
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guil->setScaleStep(scaleStep);
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guil->setScaleThresh(scaleThresh);
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guil->setPosThresh(posThresh);
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alg = guil;
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}
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vector<Vec4f> position;
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TickMeter tm;
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if (useGpu)
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{
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cuda::GpuMat d_templ(templ);
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cuda::GpuMat d_image(image);
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cuda::GpuMat d_position;
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alg->setTemplate(d_templ);
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tm.start();
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alg->detect(d_image, d_position);
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d_position.download(position);
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tm.stop();
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}
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else
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{
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alg->setTemplate(templ);
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tm.start();
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alg->detect(image, position);
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tm.stop();
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}
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cout << "Found : " << position.size() << " objects" << endl;
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cout << "Detection time : " << tm.getTimeMilli() << " ms" << endl;
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Mat out;
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cv::cvtColor(image, out, COLOR_GRAY2BGR);
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for (size_t i = 0; i < position.size(); ++i)
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{
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Point2f pos(position[i][0], position[i][1]);
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float scale = position[i][2];
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float angle = position[i][3];
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RotatedRect rect;
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rect.center = pos;
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rect.size = Size2f(templ.cols * scale, templ.rows * scale);
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rect.angle = angle;
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Point2f pts[4];
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rect.points(pts);
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line(out, pts[0], pts[1], Scalar(0, 0, 255), 3);
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line(out, pts[1], pts[2], Scalar(0, 0, 255), 3);
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line(out, pts[2], pts[3], Scalar(0, 0, 255), 3);
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line(out, pts[3], pts[0], Scalar(0, 0, 255), 3);
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}
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imshow("out", out);
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waitKey();
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return 0;
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}
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