OpenCV实践：计算轮廓的面积和周长

1、对如下图片的面积与周长进行测量

2、算法思路

1. 灰度处理
2. 模糊处理
3. 二值化
4. 形态学处理
5. 最大轮廓检测
6. 计算面积与周长

3、代码实践

# include<opencv2\opencv.hpp>
# include <iostream>
using namespace std;
using namespace cv;

int main(int argc, char** argv) {
	Mat src, dst, gray_src;
	src = imread("D://05.jpg");
	if (src.empty()) {
		cout << "can't find this picture...";
		return -1;
	}
	imshow("input", src);
	cvtColor(src, gray_src, COLOR_BGR2GRAY);
	//高斯模糊
	Mat blurImage;
	GaussianBlur(gray_src, blurImage, Size(15, 15), 0, 0);
	imshow("Gauss blur", blurImage);
	//二值化
	Mat binaryImage;
	threshold(blurImage, binaryImage, 0, 255, THRESH_BINARY | THRESH_TRIANGLE);
	imshow("binary image", binaryImage);
	//形态学处理
	Mat morphlogyImage;
	Mat kernel = getStructuringElement(MORPH_RECT, Size(3, 3), Point(-1, -1));
	morphologyEx(binaryImage, morphlogyImage, MORPH_CLOSE, kernel, Point(-1, -1), 2);
	imshow("close Image", morphlogyImage);
	//获取最大轮廓
	Mat ResultImage = Mat::zeros(src.size(), CV_8UC3);
	vector<vector<Point>> contours;
	vector<Vec4i>hierchy;
	findContours(morphlogyImage, contours, hierchy, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE, Point());//这里用RETR_EXTERNAL显示最外层轮廓，而TREE显示所有轮廓,背景像素要是黑色
	for (size_t t = 0; t < contours.size(); t++) {
		Rect rect = boundingRect(contours[t]);
		if (rect.width < src.cols / 2) continue;
		if (rect.width >(src.cols - 20)) continue;
		double area = contourArea(contours[t]);
		double lenth = arcLength(contours[t], true);
		drawContours(ResultImage, contours, static_cast<int>(t), Scalar(0, 0, 255), 2, 8, Mat());
		printf("area:%f\n", area);
		printf("lenth:%f\n", lenth);
	}
	imshow("ResultImage", ResultImage);
	waitKey(0);
	return 0;
}