OpenCv-C++-基於距離變換與分水嶺的影象分割
阿新 • • 發佈:2018-11-30
在這裡,先感謝賈志剛老師的教學,我今天學習了影象分水嶺分割,什麼是影象分割呢?借用賈志剛老師的課件,如下圖所示:
其實大致就是將下面圖1變成圖2的樣子:
圖1:
圖2:
或:
具體操作有什麼步驟?看下圖:
下面附上程式碼(具體解釋程式碼已註釋):
#include<opencv2/opencv.hpp> #include<iostream> #include<math.h> using namespace cv; using namespace std; Mat src,dst; int main(int argc, char** argv) { src = imread("D:/test/pukepai.png"); if (!src.data) { cout << "圖片未找到" << endl; return -1; } imshow("input title", src); //把白色背景變成黑色背景 for (int row = 0; row < src.rows; row++) { for (int col = 0; col < src.cols; col++) { if (src.at<Vec3b>(row, col) == Vec3b(255, 255, 255)) //3個255是白色 { src.at<Vec3b>(row, col)[0] = 0; src.at<Vec3b>(row, col)[1] = 0; src.at<Vec3b>(row, col)[2] = 0; } } } //imshow("black background", src); /*--------sharpen(使用filter2D與拉普拉斯運算元提高影象對比度)------------*/ Mat kernel = (Mat_<float>(3, 3) << 1, 1, 1, 1, -8, 1, 1, 1, 1); Mat LaplanceImg; Mat sharpImg = src; src.convertTo(sharpImg, CV_32F);//將src轉成cv_32f型別的矩陣,計算下面減法時型別要一致 /*為什麼用CV_32F,因為拉普拉斯計算的是浮點數,有正值有負值,可能會超0~255範圍*/ filter2D(src, LaplanceImg, CV_32F, kernel, Point(-1, -1),0,BORDER_DEFAULT); Mat resultImg = sharpImg - LaplanceImg; resultImg.convertTo(resultImg, CV_8UC3); LaplanceImg.convertTo(LaplanceImg, CV_8UC3); imshow("black background sharpen", resultImg); //src = resultImg; /*---------------------------------------------------------*/ /*------------------轉為二值影象(threshold)---------------*/ //先轉為灰度影象,再轉為二值影象 cvtColor(resultImg, resultImg, CV_BGR2GRAY); Mat binaryImg; threshold(resultImg, binaryImg, 40, 255, THRESH_BINARY | THRESH_OTSU);//自動確定閾值 imshow("binaryImg", binaryImg); /*---------------距離變換---------------------------------------*/ Mat distImg; distanceTransform(binaryImg, distImg, DIST_L1, 3, 5); normalize(distImg, distImg, 0, 1, NORM_MINMAX); imshow("distance Image",distImg); /*--------------將距離變換之後的結果再進行二值化-------------------------*/ Mat thres_againImg; threshold(distImg, thres_againImg, 0.4, 0.8, THRESH_BINARY); imshow("binaryImg again", thres_againImg); /*----------------------腐蝕操作(二值影象)---------------------------*/ Mat k = Mat::ones(5,5,CV_8UC1); //結構元素 erode(thres_againImg, dst, k,Point(-1,-1)); imshow("erode Image", dst); /*-----------------標記(給每一個小山頭(白色塊)編號)--------------------*/ //這裡主要使用發現輪廓和繪製輪廓 Mat dist_8u; distImg.convertTo(dist_8u, CV_8U); vector<vector<Point>> contours; findContours(dist_8u, contours, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE, Point(0, 0)); Mat markers = Mat::zeros(src.size(),CV_32SC1); for (size_t i = 0; i < contours.size(); i++) { drawContours(markers, contours, static_cast<int>(i), Scalar::all(static_cast<int>(i) + 1), -1); } circle(markers, Point(5, 5), 3, Scalar(255, 255, 255), -1); imshow("makers", markers*1000); //因為makers的值很低很低 /*----------------------------分水嶺變換------------------*/ watershed(src,markers); Mat mark = Mat::zeros(markers.size(), CV_8UC1); markers.convertTo(mark, CV_8UC1); bitwise_not(mark, mark, Mat()); imshow("watershed Image", mark); /*-------------------------著色--------------------------------*/ vector<Vec3b> colors; for (size_t i = 0; i < contours.size(); i++) { int r = theRNG().uniform(0, 255);//theRNG(),自帶的函式,隨機數生成器 int g = theRNG().uniform(0, 255); int b = theRNG().uniform(0, 255); colors.push_back(Vec3b((uchar)b, (uchar)g, (uchar)r)); } // 填充顏色並顯示 Mat colorImg = Mat::zeros(markers.size(), CV_8UC3); for (int row = 0; row < markers.rows; row++) { for (int col = 0; col < markers.cols; col++) { int index = markers.at<int>(row, col); if (index > 0 && index <= static_cast<int>(contours.size())) { colorImg.at<Vec3b>(row, col) = colors[index - 1]; } else { colorImg.at<Vec3b>(row, col) = Vec3b(0, 0, 0); } } } imshow("Finally Image", colorImg); waitKey(0); return 0; }
在此特別感謝賈志剛老師的教學!!!