【OpenCV（C++）】图像的矩

【OpenCV（C++）】图像的矩

• 矩的计算: moment()函数
• 计算轮廓面积: contourArea()函数
• 计算轮廓长度：arcLength()函数
• 程序：查找和绘制图像轮廓矩

矩函数在图像分析中有广泛的应用，如模式识别、目标分类、目标识别与方位估计、图像编码与重构等。一个从一幅数字图形中计算出来的矩集，通常描述了该图像形状的全局特征，并提供了大量的关于该图像不同类型的几何特性信息，比如大小、位置、方向及形状等。

矩的计算: moment()函数

moment函数用于计算多边形和光栅形状的最高达三阶的所有矩。矩用来计算形状的重心、面积，主轴和其它形状特征。

Moments moments(InputArray array, bool binaryImage=false)

计算轮廓面积: contourArea()函数

contourArea()函数用于计算整个轮廓或部分轮廓的面积。

double contourArea(InputArray contour, bool oriented=false)

计算轮廓长度：arcLength()函数

arcLength()函数用于计算封闭轮廓的周长或曲线的长度。

double arcLength(InputArray curve, bool closed)

程序：查找和绘制图像轮廓矩

#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include <iostream>
using namespace cv;
using namespace std;


#define WINDOW_NAME1 "【原始图】"
#define WINDOW_NAME2 "【图像轮廓】"      

Mat g_srcImage; Mat g_grayImage;
int g_nThresh = 100;
int g_nMaxThresh = 255;
RNG g_rng(12345);
Mat g_cannyMat_output;
vector<vector<Point> > g_vContours;
vector<Vec4i> g_vHierarchy;

void on_ThreshChange(int, void*);

int main(int argc, char** argv)
{
	g_srcImage = imread("a.jpg", 1);

	cvtColor(g_srcImage, g_grayImage, CV_BGR2GRAY);
	blur(g_grayImage, g_grayImage, Size(3, 3));

	namedWindow(WINDOW_NAME1, CV_WINDOW_AUTOSIZE);
	imshow(WINDOW_NAME1, g_srcImage);

	createTrackbar(" 阈值", WINDOW_NAME1, &g_nThresh, g_nMaxThresh, on_ThreshChange);
	on_ThreshChange(0, 0);

	waitKey(0);
	return(0);
}

void on_ThreshChange(int, void*)
{

	Canny(g_grayImage, g_cannyMat_output, g_nThresh, g_nThresh * 2, 3);

	findContours(g_cannyMat_output, g_vContours, g_vHierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0));

	vector<Moments> mu(g_vContours.size());
	for (unsigned int i = 0; i < g_vContours.size(); i++)
	{
		mu[i] = moments(g_vContours[i], false);
	}

	vector<Point2f> mc(g_vContours.size());
	for (unsigned int i = 0; i < g_vContours.size(); i++)
	{
		mc[i] = Point2f(static_cast<float>(mu[i].m10 / mu[i].m00), static_cast<float>(mu[i].m01 / mu[i].m00));
	}

	Mat drawing = Mat::zeros(g_cannyMat_output.size(), CV_8UC3);
	for (unsigned int i = 0; i < g_vContours.size(); i++)
	{
		Scalar color = Scalar(g_rng.uniform(0, 255), g_rng.uniform(0, 255), g_rng.uniform(0, 255));
		drawContours(drawing, g_vContours, i, color, 2, 8, g_vHierarchy, 0, Point());
		circle(drawing, mc[i], 4, color, -1, 8, 0);
	}

	namedWindow(WINDOW_NAME2, CV_WINDOW_AUTOSIZE);
	imshow(WINDOW_NAME2, drawing);

	printf("\t 输出内容: 面积和轮廓长度\n");
	for (unsigned int i = 0; i < g_vContours.size(); i++)
	{
		printf(" >通过m00计算出轮廓[%d]的面积: (M_00) = %.2f \n OpenCV函数计算出的面积=%.2f , 长度: %.2f \n\n", i, mu[i].m00, contourArea(g_vContours[i]), arcLength(g_vContours[i], true));
		Scalar color = Scalar(g_rng.uniform(0, 255), g_rng.uniform(0, 255), g_rng.uniform(0, 255));
		drawContours(drawing, g_vContours, i, color, 2, 8, g_vHierarchy, 0, Point());
		circle(drawing, mc[i], 4, color, -1, 8, 0);
	}
}

运行效果如下：

来源：CSDN

作者：姚巨龙

链接：https://blog.csdn.net/weixin_43645790/article/details/104105793