opencv平滑滤波函数（高斯滤波平滑图像）

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。

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 #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 }