opencv3阀值化下的轮廓查找与绘制

最新推荐文章于 2024-08-31 21:15:00 发布

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该博客介绍了如何使用OpenCV3进行图像阈值化处理，并通过轮廓查找和绘制操作。作者创建了滚动条窗口来动态调整阈值、轮廓绘制方式、线条粗细、颜色等参数，实现实时效果展示。通过findContours和drawContours函数，展示了在二值化图像上查找和绘制轮廓的过程。

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#include<opencv2/opencv.hpp>
#include<iostream>
#include<vector>

using namespace cv;
using namespace std;

int g_nThred = 100;
int g_nWay = 0;
int g_nThick = 3;
int g_nGrayOrBgr = 0;
int g_nColor = 0;
int g_nBlue = 255, g_nGreen = 255, g_nRed = 0;

int main()
{
	Mat srcImage = imread("group.jpg");
	imshow("【原图】", srcImage);
	
	Mat grayImage;
	cvtColor(srcImage, grayImage, CV_BGR2GRAY);

	namedWindow("【滚动条窗口】", 0);
	createTrackbar("Thred", "【滚动条窗口】", &g_nThred, 255, 0);
	createTrackbar("Way", "【滚动条窗口】", &g_nWay, 3, 0);
	createTrackbar("Thick", "【滚动条窗口】", &g_nThick, 100, 0);
	createTrackbar("Gray/Bgr", "【滚动条窗口】", &g_nGrayOrBgr, 1, 0);
	createTrackbar("Color", "【滚动条窗口】", &g_nColor, 1, 0);
	createTrackbar("Blue", "【滚动条窗口】", &g_nBlue, 255, 0);
	createTrackbar("Green", "【滚动条窗口】", &g_nGreen, 255, 0);
	createTrackbar("Red", "【滚动条窗口】", &g_nRed, 255, 0);

	Mat binImage;
	vector<vector<Point>> contours;
	vector<Vec4i> hierarchy;
	Mat dstImage;
	Scalar color;
	char key;
	while (1)
	{
		/*方式一：
		threshold(grayImage, binImage, g_nThred, 255, CV_THRESH_BINARY);
		方式二：*/
		binImage = grayImage < g_nThred;
		imshow("【阀值化后的图像】", binImage);

		//进行轮廓检测
		findContours(binImage, contours, hierarchy, CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE);
		//绘制多个轮廓，绘制一个轮廓的函数为：drawContours函数
		if (!g_nGrayOrBgr)
			srcImage.copyTo(dstImage);
		else
			grayImage.copyTo(dstImage);
		for (int i = 0; i >= 0; i = hierarchy[i][g_nWay])
		{
			if (!g_nColor)
				color = Scalar(g_nBlue, g_nGreen, g_nRed);
			else
				color = Scalar(rand() & 255, rand() & 255, rand() & 255);

			drawContours(dstImage, contours, i, color, g_nThick, 8, hierarchy);
		}
		imshow("【绘制轮廓后的图像】", dstImage);

		key = waitKey(1);
		if (key == 27)
			break;
	}

	return 0;
}