OpenCV之寻找和绘制物体的凸包例程

最新推荐文章于 2023-02-27 15:18:52 发布

LeviNinja

最新推荐文章于 2023-02-27 15:18:52 发布

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分类专栏： OpenCV 文章标签： opencv 人工智能计算机视觉

本文链接：https://blog.youkuaiyun.com/sono_io/article/details/126392072

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本文介绍了使用OpenCV库进行图像处理的基本操作，包括将图像转换为灰度图、应用模糊降噪、设置阈值进行二值化处理、寻找图像轮廓以及绘制轮廓和凸包。通过调整滚动条阈值，可以实时观察二值化效果和轮廓的变化。

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代码

#include <opencv2\opencv.hpp>
#include <vector>  
#include <algorithm> 

using namespace cv;
using namespace std;

#define WINDOW_NAME1 "[原始图窗口]"
#define WINDOW_NAME2 "[效果图窗口]"

Mat g_srcImage; Mat g_grayImage;
int g_nThresh = 0;
int g_maxThresh = 255;
RNG g_rng(12345);
Mat srcImage_copy = g_srcImage.clone();
Mat g_thresholdImage_output;
vector<vector<Point>> g_vContours;
vector<Vec4i> g_vHierarchy;

//全局函数
void on_ThreshChange(int, void*);

int main()
{
	//加载源图像
	g_srcImage = imread("333.png", 1);

	//将原图转换为灰度图并进行模糊降噪
	cvtColor(g_srcImage, g_grayImage, COLOR_BGR2GRAY);
	blur(g_grayImage, g_grayImage, Size(1, 1));

	//创建窗口并显示
	namedWindow(WINDOW_NAME1, WINDOW_AUTOSIZE);
	imshow(WINDOW_NAME1, g_grayImage);

	//创建滚动条
	createTrackbar("阈值", WINDOW_NAME1, &g_nThresh, g_maxThresh, on_ThreshChange);
	on_ThreshChange(0, 0);

	waitKey(0);
	return 0;
}

void on_ThreshChange(int, void*) {
	//对图像二值化, 控制阈值
	threshold(g_grayImage, g_thresholdImage_output, g_nThresh, 255, THRESH_BINARY);

	//寻找轮廓
	findContours(g_thresholdImage_output, g_vContours, g_vHierarchy, RETR_TREE, CHAIN_APPROX_SIMPLE, Point(0, 0));

	//遍历每个轮廓,寻找其凸包
	vector<vector<Point>> hull(g_vContours.size());
	for (int i = 0; i < g_vContours.size(); i++)
	{
		convexHull(Mat(g_vContours[i]), hull[i], false);
	}

	//绘出轮廓及其凸包
	Mat drawing = Mat::zeros(g_thresholdImage_output.size(), CV_8UC3);
	for (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, 1, 8, vector<Vec4i>(), 0, Point());
	}
	imshow(WINDOW_NAME2, drawing);
}