本文实例为大家分享了opencv实现轮廓高斯滤波平滑的具体代码，供大家参考，具体内容如下
一个小测试的题目：
在图像上点选，找到与点选处相邻的颜色相近的点集合，对该点集合提取轮廓，对该点集合轮廓平滑处理，显示处理结果。
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#include <opencv2/opencv.hpp>#include <iostream> using namespace std;using namespace cv; //************************************// Method: findRegion 漫水填充// FullName: findRegion// Access: public // Returns: vector<Point>// Qualifier:// Parameter: Mat img// Parameter: Point pos// Parameter: int LowDifference// Parameter: int UpDifference//************************************vector<Point> findRegion(Mat img, Point pos, int LowDifference, int UpDifference){ Mat image = img.clone(); Mat imgBack = img.clone(); Rect ccomp; Scalar pixel = image.at<Vec3b>(pos); Scalar pixel2 = Scalar(255 - pixel[0], 255 - pixel[1], 255 - pixel[2], pixel[3]); floodFill(image, pos, pixel2, &ccomp, Scalar(LowDifference, LowDifference, LowDifference), Scalar(UpDifference, UpDifference, UpDifference)); Mat diff; absdiff(image, imgBack, diff); //统计所有非零像素 vector<Point> pt; for (int i = 0; i < diff.rows; i++) { for (int j = 0; j < diff.cols; j++) { Point newPos(j, i); Scalar pixel3 = diff.at<Vec3b>(newPos); if (pixel3[0] != 0 || pixel3[1] != 0 || pixel3[2] != 0) { pt.push_back(newPos); } } } return pt;} //************************************// Method: findPerimeter 从点集合中寻找轮廓点// FullName: findPerimeter// Access: public // Returns: vector<Point>// Qualifier:// Parameter: vector<Point> pt// Parameter: Size size//************************************vector<Point> findPerimeter(vector<Point> pt, Size size){ Mat imgGray(size, CV_8UC1, Scalar(0)); for (int i = 0; i < pt.size(); i++) { imgGray.at<uchar>(pt[i]) = 255; } std::vector<std::vector<cv::Point>> contours; //获取轮廓不包括轮廓内的轮廓 cv::findContours(imgGray.clone(), contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE); return contours[0];} //************************************// Method: displayImage 显示图像// FullName: displayImage// Access: public // Returns: void// Qualifier:// Parameter: Mat img// Parameter: vector<Point> contours 轮廓点// Parameter: Point pos//************************************void displayImage(Mat img, vector<Point> contours, Point pos){ Mat imgShow = img.clone(); for (int i = 0; i < contours.size(); i++) { imgShow.at<Vec3b>(contours[i])[0] = 0; imgShow.at<Vec3b>(contours[i])[1] = 0; imgShow.at<Vec3b>(contours[i])[2] = 0; } circle(imgShow, pos, 3, Scalar(0, 0, 0), 1, 8, 0);//画用户选择的点 imshow("img", imgShow); waitKey(0);} //************************************// Method: findSmoothPeimeter 高斯滤波轮廓点平滑// FullName: findSmoothPeimeter// Access: public // Returns: void// Qualifier:// Parameter: Mat img 原图// Parameter: vector<Point> pt 轮廓点集合//************************************void findSmoothPeimeter(Mat img, vector<Point> pt){ vector<Point> contours = findPerimeter(pt, img.size()); Mat im; cvtColor(img, im, CV_BGR2GRAY); Mat cont = ~im; Mat original = Mat::zeros(im.rows, im.cols, CV_8UC3); Mat smoothed = img.clone(); // contour smoothing parameters for gaussian filter int filterRadius = 10; int filterSize = 2 * filterRadius + 1; double sigma = 10; size_t len = contours.size() + 2 * filterRadius; size_t idx = (contours.size() - filterRadius); vector<float> x, y; for (size_t i = 0; i < len; i++) { x.push_back(contours[(idx + i) % contours.size()].x); y.push_back(contours[(idx + i) % contours.size()].y); } // filter 1-D signals vector<float> xFilt, yFilt; GaussianBlur(x, xFilt, Size(filterSize, filterSize), sigma, sigma); GaussianBlur(y, yFilt, Size(filterSize, filterSize), sigma, sigma); // build smoothed contour vector<Point> smoothContours; for (size_t i = filterRadius; i < contours.size() + filterRadius; i++) { smoothContours.push_back(Point(xFilt[i], yFilt[i])); } Scalar color; for (int i = 0; i < smoothContours.size(); i++) { smoothed.at<Vec3b>(smoothContours[i])[0] = 0; smoothed.at<Vec3b>(smoothContours[i])[1] = 0; smoothed.at<Vec3b>(smoothContours[i])[2] = 0; } imshow("smoothed", smoothed); waitKey(10);} void main(){ Mat img = imread("4.jpg", 1); vector<Point> pt, contours; Point pos(1438, 590);//先列后行 int para1 = 2; int para2 = 2; pt = findRegion(img, pos, para1, para2); findSmoothPeimeter(img, pt); contours = findPerimeter(pt, img.size());//轮廓点集合 displayImage(img, contours, pos);//显示图像} |
漫水填充找到的轮廓
轮廓滤波平滑
以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持服务器之家。
原文链接：https://blog.csdn.net/qq_15947787/article/details/78518834
