Opencv影象處理---基於距離變換和分水嶺演算法的影象分割
阿新 • • 發佈:2018-12-16
程式碼
#include <opencv2/opencv.hpp> #include <iostream> using namespace std; using namespace cv; int main(int, char** argv) { // Load the image Mat src = imread(argv[1]); // Check if everything was fine if (!src.data) return -1; // Show source image imshow("Source Image", src); // Change the background from white to black, since that will help later to extract // better results during the use of Distance Transform for( int x = 0; x < src.rows; x++ ) { for( int y = 0; y < src.cols; y++ ) { if ( src.at<Vec3b>(x, y) == Vec3b(255,255,255) ) { src.at<Vec3b>(x, y)[0] = 0; src.at<Vec3b>(x, y)[1] = 0; src.at<Vec3b>(x, y)[2] = 0; } } } // Show output image imshow("Black Background Image", src); // Create a kernel that we will use for accuting/sharpening our image Mat kernel = (Mat_<float>(3,3) << 1, 1, 1, 1, -8, 1, 1, 1, 1); // an approximation of second derivative, a quite strong kernel // do the laplacian filtering as it is // well, we need to convert everything in something more deeper then CV_8U // because the kernel has some negative values, // and we can expect in general to have a Laplacian image with negative values // BUT a 8bits unsigned int (the one we are working with) can contain values from 0 to 255 // so the possible negative number will be truncated Mat imgLaplacian; Mat sharp = src; // copy source image to another temporary one filter2D(sharp, imgLaplacian, CV_32F, kernel); src.convertTo(sharp, CV_32F); Mat imgResult = sharp - imgLaplacian; // convert back to 8bits gray scale imgResult.convertTo(imgResult, CV_8UC3); imgLaplacian.convertTo(imgLaplacian, CV_8UC3); // imshow( "Laplace Filtered Image", imgLaplacian ); imshow( "New Sharped Image", imgResult ); src = imgResult; // copy back // Create binary image from source image Mat bw; cvtColor(src, bw, CV_BGR2GRAY); threshold(bw, bw, 40, 255, CV_THRESH_BINARY | CV_THRESH_OTSU); imshow("Binary Image", bw); // Perform the distance transform algorithm Mat dist; distanceTransform(bw, dist, CV_DIST_L2, 3); // Normalize the distance image for range = {0.0, 1.0} // so we can visualize and threshold it normalize(dist, dist, 0, 1., NORM_MINMAX); imshow("Distance Transform Image", dist); // Threshold to obtain the peaks // This will be the markers for the foreground objects threshold(dist, dist, .4, 1., CV_THRESH_BINARY); // Dilate a bit the dist image Mat kernel1 = Mat::ones(3, 3, CV_8UC1); dilate(dist, dist, kernel1); imshow("Peaks", dist); // Create the CV_8U version of the distance image // It is needed for findContours() Mat dist_8u; dist.convertTo(dist_8u, CV_8U); // Find total markers vector<vector<Point> > contours; findContours(dist_8u, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE); // Create the marker image for the watershed algorithm Mat markers = Mat::zeros(dist.size(), CV_32SC1); // Draw the foreground markers for (size_t i = 0; i < contours.size(); i++) drawContours(markers, contours, static_cast<int>(i), Scalar::all(static_cast<int>(i)+1), -1); // Draw the background marker circle(markers, Point(5,5), 3, CV_RGB(255,255,255), -1); imshow("Markers", markers*10000); // Perform the watershed algorithm watershed(src, markers); Mat mark = Mat::zeros(markers.size(), CV_8UC1); markers.convertTo(mark, CV_8UC1); bitwise_not(mark, mark); // imshow("Markers_v2", mark); // uncomment this if you want to see how the mark // image looks like at that point // Generate random colors vector<Vec3b> colors; for (size_t i = 0; i < contours.size(); i++) { int b = theRNG().uniform(0, 255); int g = theRNG().uniform(0, 255); int r = theRNG().uniform(0, 255); colors.push_back(Vec3b((uchar)b, (uchar)g, (uchar)r)); } // Create the result image Mat dst = Mat::zeros(markers.size(), CV_8UC3); // Fill labeled objects with random colors for (int i = 0; i < markers.rows; i++) { for (int j = 0; j < markers.cols; j++) { int index = markers.at<int>(i,j); if (index > 0 && index <= static_cast<int>(contours.size())) dst.at<Vec3b>(i,j) = colors[index-1]; else dst.at<Vec3b>(i,j) = Vec3b(0,0,0); } } // Visualize the final image imshow("Final Result", dst); waitKey(0); return 0; }
解釋與結果
- 載入源影象並檢查它是否載入沒有任何問題,然後顯示它:
- 如果我們有一個白色背景影象,最好將其轉換為黑色。 當我們應用距離變換時,這將有助於我們更容易區分前景物件:
- 我們將銳化我們的影象,以銳化前景物件的邊緣。 我們將應用具有相當強濾波器的拉普拉斯濾波器(近似二階導數):
- 現在我們將新的銳化源影象分別轉換為灰度和二進位制:
- 我們現在準備在二進位制影象上應用距離變換。 此外,我們對輸出影象進行標準化,以便能夠對結果進行視覺化和閾值處理:
- 我們對dist影象進行閾值處理,然後執行一些形態學操作(即擴張),以便從上面的影象中提取峰值:
- 我們在cv :: findContours函式的幫助下為分水嶺演算法建立種子/標記:
- 最後,我們可以應用分水嶺演算法,並可視化結果: