opencv3编程入门（第十章10.1-10.3）

10.1Harris角点检测

#include<opencv2/highgui/highgui.hpp>
#include<opencv2/imgproc/imgproc.hpp>
#include<opencv2/opencv.hpp>
#include<iostream>
using namespace cv;
using namespace std;
int main()
{
	Mat srcImage = imread("ying.jpg", 0);//以灰度模式载入图像
	imshow("原始图", srcImage);
	Mat cornerStrength;//进行Harris角点检测
	cornerHarris(srcImage, cornerStrength, 2, 3, 0.01);
	Mat harrisCorner;//对灰度图进行阈值，得到二值图像
	threshold(cornerStrength, harrisCorner, 0.00001, 255, THRESH_BINARY);
	imshow("角点检测后的二值效果图", harrisCorner);
	imwrite("ying1.jpg",harrisCorner);
	waitKey(0);
	return 0;
}

程序原始图：

程序执行后的结果：

10.15harris角点检测综合示例程序：

#include<opencv2/highgui/highgui.hpp>
#include<opencv2/imgproc/imgproc.hpp>
#include<opencv2/opencv.hpp>
#include<iostream>
using namespace cv;
using namespace std;
#define WINDOW_NAME1 "程序窗口1"
#define WINDOW_NAME2 "程序窗口2"
Mat g_srcImage, g_srcImage1, g_grayImage;
int thresh = 30;
int max_thresh = 175;
void on_CornerHarris(int, void*);
int main(int argc,char** argv)
{
	g_srcImage = imread("huashan.jpg", 1);
	if (!g_srcImage.data) { cout << "fail to load image" << endl;return false; }
	imshow("原始图", g_srcImage);
	g_srcImage1 = g_srcImage.clone();//克隆原始图
	cvtColor(g_srcImage, g_grayImage,COLOR_BGR2GRAY);//灰度变换
	namedWindow(WINDOW_NAME1, WINDOW_AUTOSIZE);
	createTrackbar("阈值", WINDOW_NAME1, &thresh, max_thresh, on_CornerHarris);
	on_CornerHarris(0, 0);//调用一次回调函数并初始化
	waitKey(0);
	return(0);
}
void on_CornerHarris(int, void*)
{
	Mat dstImage, normImage, scaledImage;
	//置零当前需要显示的两幅图，即清楚上一次调用次函数是他们得值
	dstImage = Mat::zeros(g_srcImage.size(), CV_32FC1);
	g_srcImage1 = g_srcImage.clone();
	cornerHarris(g_grayImage, dstImage, 2, 3, 0.04, BORDER_DEFAULT);
	normalize(dstImage, normImage, 0, 255, NORM_MINMAX, CV_32FC1, Mat());
	convertScaleAbs(normImage, scaledImage);//将归一化后的图变换成8位无符号整型
	for (int j = 0;j < normImage.rows;j++)//将符合阈值条件的角点绘制出来
	{
		for (int i = 0;i < normImage.cols;i++)
		{
			if ((int)normImage.at<float>(j, i)>thresh + 80)
			{
				circle(g_srcImage, Point(i, j), 5, Scalar(10, 10, 255), 2, 8, 0);
				circle(scaledImage, Point(i, j), 5, Scalar(0, 10, 255), 2, 8, 0);
			}
		}
	}
	imshow(WINDOW_NAME1, g_srcImage1);
	imshow(WINDOW_NAME2, scaledImage);
}

程序执行结果如图：

10.2 Shi-Tomasi角点检测：

#include<opencv2/highgui/highgui.hpp>
#include<opencv2/imgproc/imgproc.hpp>
#include<opencv2/opencv.hpp>
#include<iostream>
using namespace cv;
using namespace std;
#define WINDOW_NAME "Shi-Tomasi角点检测"

Mat g_srcImage,  g_grayImage;
int   g_maxCornerNumber = 33;
int g_maxTrackbarNumber = 500;
RNG g_rng(12345);
void on_GoodFeaturesToTrack(int, void*)
{
	if (g_maxCornerNumber <= 1) { g_maxCornerNumber = 1; }//变量小于1时，变量置1
	vector<Point2f>corners;
	double qualityLevel=0.01;//角点检测可接受的最小特征值
	double minDistance = 10;//角点之间的最小距离
	int blockSize = 3;//计算导数自相关矩阵时指定的邻域范围
	double k = 0.04;//权重系数
	Mat copy = g_srcImage.clone();
	goodFeaturesToTrack(g_grayImage, corners, g_maxCornerNumber, qualityLevel, 
		minDistance, Mat(), blockSize, false, k);
	//g_maxCornerNumber：角点的最大数量， Mat()：感兴趣区域，false：不使用Harris角点检测测
	cout << "此次检测到的角点数量为：" << corners.size() << endl;
	int r = 4;//绘制检测到的角点
	for (unsigned int i = 0;i < corners.size();i++)
	{
		circle(copy, corners[i], r, Scalar(g_rng.uniform(0, 255), g_rng.uniform(0, 255),
			g_rng.uniform(0, 255)), -1, 8, 0);
		imshow(WINDOW_NAME, copy);
	}
}
int main()
{
	g_srcImage = imread("huashan.jpg", 1);
	cvtColor(g_srcImage, g_grayImage, COLOR_BGR2GRAY);
	namedWindow(WINDOW_NAME, WINDOW_AUTOSIZE);
	createTrackbar("最大角点数", WINDOW_NAME, &g_maxCornerNumber, 
		g_maxTrackbarNumber, on_GoodFeaturesToTrack);
	imshow(WINDOW_NAME, g_srcImage);
	on_GoodFeaturesToTrack(0, 0);
	waitKey(0);
	return 0;
}

程序执行结果如图：

10.3亚像素级角点检测

#include<opencv2/highgui/highgui.hpp>
#include<opencv2/imgproc/imgproc.hpp>
#include<opencv2/opencv.hpp>
#include<iostream>
using namespace cv;
using namespace std;
#define WINDOW_NAME "Shi-Tomasi角点检测"

Mat g_srcImage,  g_grayImage;
int   g_maxCornerNumber = 33;
int g_maxTrackbarNumber = 500;
RNG g_rng(12345);
void on_GoodFeaturesToTrack(int, void*)
{
	if (g_maxCornerNumber <= 1) { g_maxCornerNumber = 1; }//变量小于1时，变量置1
	vector<Point2f>corners;
	double qualityLevel=0.01;//角点检测可接受的最小特征值
	double minDistance = 10;//角点之间的最小距离
	int blockSize = 3;//计算导数自相关矩阵时指定的邻域范围
	double k = 0.04;//权重系数
	Mat copy = g_srcImage.clone();
	goodFeaturesToTrack(g_grayImage, corners, g_maxCornerNumber, qualityLevel, 
		minDistance, Mat(), blockSize, false, k);
	//g_maxCornerNumber：角点的最大数量， Mat()：感兴趣区域，false：不使用Harris角点检测测
	cout << "此次检测到的角点数量为：" << corners.size() << endl;
	int r = 4;//绘制检测到的角点
	for (unsigned int i = 0;i < corners.size();i++)
	{
		circle(copy, corners[i], r, Scalar(g_rng.uniform(0, 255), g_rng.uniform(0, 255),
			g_rng.uniform(0, 255)), -1, 8, 0);
		imshow(WINDOW_NAME, copy);
		Size winSize = Size(5, 5);//搜索窗口的一半尺寸
		Size zeroZone = Size(-1, -1);//死区的一半尺寸.(-1,-1,)表示没有死区
		TermCriteria criteria = TermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 40, 0.001);
		//criteria:角点迭代过程的终止条件，可以是最大迭代数目，或设定的精确度
		cornerSubPix(g_grayImage, corners, winSize, zeroZone, criteria);
		for (int i = 0;i < corners.size();i++)
		{
			cout << "\t精确角点坐标[" << i << "](" << corners[i].x << "," << corners[i].y << ")" << endl;
		}
	}
}
int main()
{
	g_srcImage = imread("huashan.jpg", 1);
	cvtColor(g_srcImage, g_grayImage, COLOR_BGR2GRAY);
	namedWindow(WINDOW_NAME, WINDOW_AUTOSIZE);
	createTrackbar("最大角点数", WINDOW_NAME, &g_maxCornerNumber, 
		g_maxTrackbarNumber, on_GoodFeaturesToTrack);
	imshow(WINDOW_NAME, g_srcImage);
	on_GoodFeaturesToTrack(0, 0);
	waitKey(0);
	return 0;
}

程序执行结果如图：