C++ OpenCV实战之标记点检测的实现

在实际应用中，能够直接利用霍夫圆检测这些理想方法的应用场景是非常少的，更多的是利用拟合的办法去寻找圆形。

大致思路如下，首先先选择要处理的ROI部分，记录下该图的左上点在原图的坐标，如果原图过大，要先进行等比例缩放；然后利用自适应阈值和Canny边缘提取进行处理，再进行闭运算与轮廓检测，计算点集面积，通过筛选面积阈值去除杂点，最后进行轮廓检测，拟合椭圆，效果如下：

1.导入原图：

2.截取ROI

3.进行自适应阈值化与Canny边缘提取

4.进行闭运算，然后轮廓检测，然后计算点集面积，通过面积阈值去除杂点

5.再次进行轮廓检测，拟合椭圆

代码如下：

#include <opencv2\highgui\highgui.hpp>
#include <opencv2\imgproc\imgproc.hpp>
#include <opencv2\core\core.hpp>
#include <iostream>

#define scale 2//图像缩放因子
#define cannythreshold 80

typedef struct _ROIStruct
{
	cv::Point2d ROIPoint;
	cv::Mat ROIImage;
}ROIStruct;

ROIStruct getROI(cv::Mat src,bool flag = false)
{
	ROIStruct ROI_Struct;
	cv::Rect2d ROIrect = selectROI(src);
	ROI_Struct.ROIPoint = ROIrect.tl();//获取ROI区域左上角的点
	ROI_Struct.ROIImage = src(ROIrect);

	if (flag == true)
	{
		cv::imshow("ROI", ROI_Struct.ROIImage);
	}

	return ROI_Struct;
}
int main()
{
	cv::Mat srcImage = cv::imread("7.jpg");//读取待处理的图片
	cv::resize(srcImage, srcImage, cv::Size(srcImage.cols / scale, srcImage.rows / scale));//图像缩放,否则原来图像会在ROI时显示不下

	ROIStruct ROI = getROI(srcImage);//选择ROI区域

	cv::Mat DetectImage, thresholdImage;
	ROI.ROIImage.copyTo(DetectImage);
	cv::imshow("ROI", DetectImage);
	cv::cvtColor(DetectImage, thresholdImage, CV_RGB2GRAY);
	cv::adaptiveThreshold(thresholdImage, thresholdImage, 255, CV_ADAPTIVE_THRESH_GAUSSIAN_C, CV_THRESH_BINARY,11,35);//自适应阈值
	cv::Canny(thresholdImage, thresholdImage, cannythreshold, cannythreshold * 3, 3);

	cv::imshow("thresholdImage", thresholdImage);

	std::vector<std::vector<cv::Point>> contours1;
	std::vector<cv::Vec4i> hierarchy1;
	cv::Mat element = cv::getStructuringElement(cv::MORPH_ELLIPSE, cv::Size(3, 3));
	cv::morphologyEx(thresholdImage, thresholdImage, cv::MORPH_CLOSE, element,cv::Point(-1,-1),2);
	cv::Mat findImage = cv::Mat::zeros(thresholdImage.size(), CV_8UC3);

	cv::findContours(thresholdImage, contours1, hierarchy1,CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE);

	for (int i = 0; i <contours1.size();i++)
	{
		double area = cv::contourArea(contours1[i]);
		//std::cout << i << "点集区域面积:" << area << std::endl;

		if (area < 120)
		{
			continue;
		}

		else
		{
			drawContours(findImage, contours1, i, cv::Scalar(255, 255, 255), -1, 8, cv::Mat(), 0, cv::Point());
		}

	}

	cv::imshow("drawing", findImage);

	cv::Mat CircleImage(findImage.size(),CV_8UC1);
	findImage.copyTo(CircleImage);
	cv::cvtColor(CircleImage, CircleImage, CV_RGB2GRAY);

	std::vector<std::vector<cv::Point>> contours2;
	std::vector<cv::Vec4i> hierarchy2;

	cv::Mat resultImage(CircleImage.size(), CV_8UC3);
	cv::findContours(CircleImage, contours2, hierarchy2, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE);
	std::vector<cv::Point> points1, points2;

	cv::Mat compareImage;
	DetectImage.copyTo(compareImage);

	for (int j = 0; j <contours2.size();j++)
	{
		cv::RotatedRect box = cv::fitEllipse(contours2[j]);
		points1.push_back(box.center);
		ellipse(resultImage, box, cv::Scalar(0, 0, 255), 1, CV_AA);
		ellipse(compareImage, box, cv::Scalar(0, 0, 255), 1, CV_AA);
	}

	for (int i = 0; i < points1.size(); i++)
	{
		cv::Point ans;
		ans.x = ROI.ROIPoint.x + points1[i].x;
		ans.x = ans.x*scale;
		ans.y = ROI.ROIPoint.y + points1[i].y;
		ans.y = ans.y*scale;
		points2.push_back(ans);
	}

	std::cout << points1 << std::endl;
	std::cout << ROI.ROIPoint << std::endl;
	std::cout << points2 << std::endl;

	cv::imshow("resultImage", resultImage);
	cv::imshow("compareImage", compareImage);
	cv::waitKey(0);
	return 0;
}

到此这篇关于C++ OpenCV实战之标记点检测的实现的文章就介绍到这了,更多相关C++ OpenCV标记点检测内容请搜索我们以前的文章或继续浏览下面的相关文章希望大家以后多多支持我们！