Aruco Marker Detect标记检测-小明编程

想了解背后算法可以参考文章，或者阅读源码，我在这里只是调用下函数接口，对算法不了解。

加个关键字：KinectV2和AR结合，OpenCV AR SDK

以下是代码，注意你得有aruco的库，木有的话要去下载才行。

// ArucoVideoCapture.cpp : 定义控制台应用程序的入口点。
//

#include "stdafx.h"
#include "opencv-include/opencv2/aruco.hpp"
#include "opencv-include/opencv2/opencv.hpp"
#include <iostream>

using namespace std;
using namespace cv;


int main(int argc, char *argv[]) {
	VideoCapture inputVideo;
	int waitTime;
	inputVideo.open(0);
	inputVideo.set(CAP_PROP_FRAME_WIDTH, 1280);
	inputVideo.set(CAP_PROP_FRAME_HEIGHT, 720);
	waitTime = 10;

	double totalTime = 0;
	int totalIterations = 0;

	Ptr<aruco::Dictionary> dictionary = aruco::getPredefinedDictionary(aruco::DICT_ARUCO_ORIGINAL);
	Mat out;
	dictionary->drawMarker(100, 600, out, 5);

	Ptr<aruco::DetectorParameters> detectorParams = aruco::DetectorParameters::create();
	detectorParams->minDistanceToBorder = 0;

	while (inputVideo.grab()) {
		Mat image, imageCopy;
		inputVideo.retrieve(image);
		double tick = (double)getTickCount();
		vector< int > ids;
		vector< vector< Point2f > > corners, rejected;
		vector< Vec3d > rvecs, tvecs;
		// detect markers and estimate pose
		aruco::detectMarkers(image, dictionary, corners, ids, detectorParams, rejected);

		double currentTime = ((double)getTickCount() - tick) / getTickFrequency();
		totalTime += currentTime;
		totalIterations++;
		// draw results
		image.copyTo(imageCopy);
		if (ids.size() > 0) 
		{
			aruco::drawDetectedMarkers(imageCopy, corners, ids);
			if (totalIterations % 30 == 0) {
				cout << "Detection Time = " << currentTime * 1000 << " ms "
					<< "(Mean = " << 1000 * totalTime / double(totalIterations) << " ms)" << endl;
			}
		}

		imshow("out", imageCopy);
		char key = (char)waitKey(waitTime);
		if (key == 27) break;
	}

	return 0;
}

// ArucoVideoCapture.cpp : 定义控制台应用程序的入口点。

#include "stdafx.h"

#include "opencv-include/opencv2/aruco.hpp"

#include "opencv-include/opencv2/opencv.hpp"

#include <iostream>

using namespace std;

using namespace cv;

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

VideoCapture inputVideo;

int waitTime;

inputVideo.open(0);

inputVideo.set(CAP_PROP_FRAME_WIDTH, 1280);

inputVideo.set(CAP_PROP_FRAME_HEIGHT, 720);

waitTime = 10;

double totalTime = 0;

int totalIterations = 0;

Ptr<aruco::Dictionary> dictionary = aruco::getPredefinedDictionary(aruco::DICT_ARUCO_ORIGINAL);

Mat out;

dictionary->drawMarker(100, 600, out, 5);

Ptr<aruco::DetectorParameters> detectorParams = aruco::DetectorParameters::create();

detectorParams->minDistanceToBorder = 0;

while (inputVideo.grab()) {

Mat image, imageCopy;

inputVideo.retrieve(image);

double tick = (double)getTickCount();

vector< int > ids;

vector< vector< Point2f > > corners, rejected;

vector< Vec3d > rvecs, tvecs;

// detect markers and estimate pose

aruco::detectMarkers(image, dictionary, corners, ids, detectorParams, rejected);

double currentTime = ((double)getTickCount() - tick) / getTickFrequency();

totalTime += currentTime;

totalIterations++;

// draw results

image.copyTo(imageCopy);

if (ids.size() > 0)

{

aruco::drawDetectedMarkers(imageCopy, corners, ids);

if (totalIterations % 30 == 0) {

cout << "Detection Time = " << currentTime * 1000 << " ms "

<< "(Mean = " << 1000 * totalTime / double(totalIterations) << " ms)" << endl;

}

imshow("out", imageCopy);

char key = (char)waitKey(waitTime);

if (key == 27) break;

}

return 0;

}

Aruco Marker Detect标记检测

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