OpenCV learning basic image operations (12): Hough transform

Hough Transform

Polar coordinates

Line detection 

Process steps:

• Transform points in spatial coordinates to polar coordinate space (Hough space) through polar coordinates
• The lines represented by the intersecting points in the Hough space are the lines where these points are located
• Then use polar coordinates to inversely transform the points selected by Hough in the Hough space back to straight lines in rectangular coordinates.

Circle detection

Process steps

•  Perform median filtering on the picture first, because the Hough circle detection is more sensitive to the salt and pepper noise in the picture
• Perform canny edge detection, first filter out points that may be edges
• Use parametric equations to convert Cartesian coordinate system to polar coordinate system

              

• Assuming that each pixel is the center (a, b), after determining (a, b), change θ and R to draw multiple circles, and when these circles intersect at one point, it is the center of a circle, and the radius is There may be multiple (determined by the distance from those points to this point)

API introduction

Line detection

HoughLines(
InputArray src,//输入图像，必须是8-bit的灰度图
OutputArray lines,//输出图像，是极坐标形式的直线
double rho,//生成极坐标时候的像素扫描步长，以像素为单位的距离步长。
double theta,//生成极坐标时候的角度扫描步长，以弧度为单位的角度步长。
int threshold,//阈值，只有获得足够交点的极坐标才会被视为是一条有效的直线
double srn=0,//默认值为0，用于在多尺度霍夫变换中作为参数rho的除数，rho=rho/srn。
double stn=0,//默认值为0，用于在多尺度霍夫变换中作为参数theta的除数，theta=theta/stn。
double min_theta,//表示角度扫描范围0~180之间，默认即可
double max_theta = CV_PI//
)
//需要自己将他反变换回直角坐标系中

HoughLinesP
(
InputArray src,//输入图像，必须是8-bit的灰度图
OutputArray lines,//输出直线，是直角坐标形式的直线
double rho,//生成极坐标时候的像素扫描步长
double theta,//生成极坐标时候的角度扫描步长，通常取CV_PI/180
double minLineLength = 0,//最小直线长
double maxLineGap = 0，//最大间隔，即直线与直线之间的间隔 
)
//直接返回直角坐标系中的直线

Circle detection

HoughCircles(
    InputArray src,//输入图像，必须是8-bit的灰度图
    OutputArray lines,//输出结果，发现圆的信息
    Int method,//方法 HOUGH_GRADENT 基于梯度的
    Double dp,//dp = 1 ,DIP,即尺度
    Double mindist,//两个圆弧相隔的最短距离，比这个距离小就认为是同一个圆，大于则认为是同心圆
    Double param1,//canny的弱边缘阈值
    Double param2,//中心点累加器的阈值，即有多数个像素在同一弧度上，才判断为圆，设定此值需要参考半径
    Int minradiux,//最小半径
    Int maxradiux,//最大半径
)

Code and practice

Line detection

#include <iostream>
#include <math.h>
#include <opencv2/opencv.hpp>

using namespace cv;
using namespace std;

int main(int argc, char* argv[])
{
	//src = imread("src.jpg");
	Mat src = imread("1.PNG");
	Mat edge_img;
	if (!src.data)
	{
		cout << "cannot open image" << endl;
		return -1;
	}
	Mat gray_src;
	cvtColor(src, gray_src, COLOR_BGR2GRAY);
	Canny(src, edge_img, 150, 250);
	
	
	imshow("edge image", edge_img);

	vector<Vec4f> plines;
	//创建向量存储检测结果
	HoughLinesP(edge_img, plines, 1, CV_PI/180.0, 10, 0, 5);
	//检测直线
	Scalar color = Scalar(0, 0, 255);

	for (size_t i = 0; i < plines.size(); i++)
	{
		Vec4f hline = plines[i];
		line(gray_src, Point(hline[0], hline[1]), Point(hline[2], hline[3]), color, 3, LINE_AA);
	}
	imshow("OUTPUT", gray_src);
	
	waitKey(0);
	
	return 0;
}

#include <iostream>
#include <math.h>
#include <opencv2/opencv.hpp>

using namespace cv;
using namespace std;


int main(int argc, char* argv[])
{
	Mat src = imread("5.jpg");
	//Mat src = imread("1.PNG");
	Mat edge_img;
	if (!src.data)
	{
		cout << "cannot open image" << endl;
		return -1;
	}

	Mat moutput;
	medianBlur(src, moutput,3);
	cvtColor(moutput, moutput, COLOR_BGR2GRAY);


	vector<Vec3f> pcircles;
	//创建向量存储检测结果
	HoughCircles(moutput, pcircles, HOUGH_GRADIENT, 1, 15, 30, 35, 15, 50);
	//检测圆
	Scalar color = Scalar(0, 255, 0);
	for (size_t i = 0; i < pcircles.size(); i++)
	{
		Vec3f cc = pcircles[i];
		circle(src, Point(cc[0], cc[1]), cc[2], color, 2);
		circle(src, Point(cc[0], cc[1]), 2, color, 2);
	}
	imshow("OUTPUT", src);
	
	waitKey(0);
	
	return 0;
}