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Move gpu sample to gpu folder

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This commit is contained in:
Andrey Kamaev
2012-09-10 19:17:50 +04:00
parent 200a81746e
commit 36f912261b
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samples/cpp/generalized_hough.cpp
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#include <vector>
#include <iostream>
#include <string>
#include "opencv2/core/core.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/gpu/gpu.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/contrib/contrib.hpp"
using namespace std;
using namespace cv;
using namespace cv::gpu;
static Mat loadImage(const string& name)
{
Mat image = imread(name, IMREAD_GRAYSCALE);
if (image.empty())
{
cerr << "Can't load image - " << name << endl;
exit(-1);
}
return image;
}
int main(int argc, const char* argv[])
{
CommandLineParser cmd(argc, argv,
"{ image i | pic1.png | input image }"
"{ template t | templ.png | template image }"
"{ scale s | | estimate scale }"
"{ rotation r | | estimate rotation }"
"{ gpu | | use gpu version }"
"{ minDist | 100 | minimum distance between the centers of the detected objects }"
"{ levels | 360 | R-Table levels }"
"{ votesThreshold | 30 | the accumulator threshold for the template centers at the detection stage. The smaller it is, the more false positions may be detected }"
"{ angleThresh | 10000 | angle votes treshold }"
"{ scaleThresh | 1000 | scale votes treshold }"
"{ posThresh | 100 | position votes threshold }"
"{ dp | 2 | inverse ratio of the accumulator resolution to the image resolution }"
"{ minScale | 0.5 | minimal scale to detect }"
"{ maxScale | 2 | maximal scale to detect }"
"{ scaleStep | 0.05 | scale step }"
"{ minAngle | 0 | minimal rotation angle to detect in degrees }"
"{ maxAngle | 360 | maximal rotation angle to detect in degrees }"
"{ angleStep | 1 | angle step in degrees }"
"{ maxSize | 1000 | maximal size of inner buffers }"
"{ help h ? | | print help message }"
);
cmd.about("This program demonstrates arbitary 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 estimateScale = cmd.has("scale");
const bool estimateRotation = cmd.has("rotation");
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 maxSize = cmd.get<int>("maxSize");
if (!cmd.check())
{
cmd.printErrors();
return -1;
}
Mat templ = loadImage(templName);
Mat image = loadImage(imageName);
int method = GHT_POSITION;
if (estimateScale)
method += GHT_SCALE;
if (estimateRotation)
method += GHT_ROTATION;
vector<Vec4f> position;
cv::TickMeter tm;
if (useGpu)
{
GpuMat d_templ(templ);
GpuMat d_image(image);
GpuMat d_position;
Ptr<GeneralizedHough_GPU> d_hough = GeneralizedHough_GPU::create(method);
d_hough->set("minDist", minDist);
d_hough->set("levels", levels);
d_hough->set("dp", dp);
d_hough->set("maxSize", maxSize);
if (estimateScale && estimateRotation)
{
d_hough->set("angleThresh", angleThresh);
d_hough->set("scaleThresh", scaleThresh);
d_hough->set("posThresh", posThresh);
}
else
{
d_hough->set("votesThreshold", votesThreshold);
}
if (estimateScale)
{
d_hough->set("minScale", minScale);
d_hough->set("maxScale", maxScale);
d_hough->set("scaleStep", scaleStep);
}
if (estimateRotation)
{
d_hough->set("minAngle", minAngle);
d_hough->set("maxAngle", maxAngle);
d_hough->set("angleStep", angleStep);
}
d_hough->setTemplate(d_templ);
tm.start();
d_hough->detect(d_image, d_position);
d_hough->download(d_position, position);
tm.stop();
}
else
{
Ptr<GeneralizedHough> hough = GeneralizedHough::create(method);
hough->set("minDist", minDist);
hough->set("levels", levels);
hough->set("dp", dp);
if (estimateScale && estimateRotation)
{
hough->set("angleThresh", angleThresh);
hough->set("scaleThresh", scaleThresh);
hough->set("posThresh", posThresh);
hough->set("maxSize", maxSize);
}
else
{
hough->set("votesThreshold", votesThreshold);
}
if (estimateScale)
{
hough->set("minScale", minScale);
hough->set("maxScale", maxScale);
hough->set("scaleStep", scaleStep);
}
if (estimateRotation)
{
hough->set("minAngle", minAngle);
hough->set("maxAngle", maxAngle);
hough->set("angleStep", angleStep);
}
hough->setTemplate(templ);
tm.start();
hough->detect(image, position);
tm.stop();
}
cout << "Found : " << position.size() << " objects" << endl;
cout << "Detection time : " << tm.getTimeMilli() << " ms" << endl;
Mat out;
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;
}