diff options
Diffstat (limited to 'samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp')
| -rw-r--r-- | samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp | 113 |
1 files changed, 58 insertions, 55 deletions
diff --git a/samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp b/samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp index 87a5436a6d..d038cbd874 100644 --- a/samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp +++ b/samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp @@ -1,5 +1,4 @@ /** - * @function Watershed_and_Distance_Transform.cpp * @brief Sample code showing how to segment overlapping objects using Laplacian filtering, in addition to Watershed and Distance Transformation * @author OpenCV Team */ @@ -12,43 +11,47 @@ using namespace std; using namespace cv; -int main() +int main(int argc, char *argv[]) { -//! [load_image] + //! [load_image] // Load the image - Mat src = imread("../data/cards.png"); - - // Check if everything was fine - if (!src.data) + CommandLineParser parser( argc, argv, "{@input | ../data/cards.png | input image}" ); + Mat src = imread( parser.get<String>( "@input" ) ); + if( src.empty() ) + { + cout << "Could not open or find the image!\n" << endl; + cout << "Usage: " << argv[0] << " <Input image>" << endl; return -1; + } // Show source image imshow("Source Image", src); -//! [load_image] + //! [load_image] -//! [black_bg] + //! [black_bg] // 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; - } + for ( int i = 0; i < src.rows; i++ ) { + for ( int j = 0; j < src.cols; j++ ) { + if ( src.at<Vec3b>(i, j) == Vec3b(255,255,255) ) + { + src.at<Vec3b>(i, j)[0] = 0; + src.at<Vec3b>(i, j)[1] = 0; + src.at<Vec3b>(i, j)[2] = 0; + } } } // Show output image imshow("Black Background Image", src); -//! [black_bg] + //! [black_bg] -//! [sharp] - // Create a kernel that we will use for accuting/sharpening our image + //! [sharp] + // Create a kernel that we will use to sharpen 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 + 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 @@ -57,8 +60,8 @@ int main() // 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); + filter2D(src, imgLaplacian, CV_32F, kernel); + Mat sharp; src.convertTo(sharp, CV_32F); Mat imgResult = sharp - imgLaplacian; @@ -68,41 +71,39 @@ int main() // imshow( "Laplace Filtered Image", imgLaplacian ); imshow( "New Sharped Image", imgResult ); -//! [sharp] + //! [sharp] - src = imgResult; // copy back - -//! [bin] + //! [bin] // Create binary image from source image Mat bw; - cvtColor(src, bw, COLOR_BGR2GRAY); + cvtColor(imgResult, bw, COLOR_BGR2GRAY); threshold(bw, bw, 40, 255, THRESH_BINARY | THRESH_OTSU); imshow("Binary Image", bw); -//! [bin] + //! [bin] -//! [dist] + //! [dist] // Perform the distance transform algorithm Mat dist; distanceTransform(bw, dist, 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); + normalize(dist, dist, 0, 1.0, NORM_MINMAX); imshow("Distance Transform Image", dist); -//! [dist] + //! [dist] -//! [peaks] + //! [peaks] // Threshold to obtain the peaks // This will be the markers for the foreground objects - threshold(dist, dist, .4, 1., THRESH_BINARY); + threshold(dist, dist, 0.4, 1.0, THRESH_BINARY); // Dilate a bit the dist image - Mat kernel1 = Mat::ones(3, 3, CV_8UC1); + Mat kernel1 = Mat::ones(3, 3, CV_8U); dilate(dist, dist, kernel1); imshow("Peaks", dist); -//! [peaks] + //! [peaks] -//! [seeds] + //! [seeds] // Create the CV_8U version of the distance image // It is needed for findContours() Mat dist_8u; @@ -113,34 +114,36 @@ int main() findContours(dist_8u, contours, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE); // Create the marker image for the watershed algorithm - Mat markers = Mat::zeros(dist.size(), CV_32SC1); + Mat markers = Mat::zeros(dist.size(), CV_32S); // 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); + { + drawContours(markers, contours, static_cast<int>(i), Scalar(static_cast<int>(i)+1), -1); + } // Draw the background marker - circle(markers, Point(5,5), 3, CV_RGB(255,255,255), -1); + circle(markers, Point(5,5), 3, Scalar(255), -1); imshow("Markers", markers*10000); -//! [seeds] + //! [seeds] -//! [watershed] + //! [watershed] // Perform the watershed algorithm - watershed(src, markers); + watershed(imgResult, markers); - Mat mark = Mat::zeros(markers.size(), CV_8UC1); - markers.convertTo(mark, CV_8UC1); + Mat mark; + markers.convertTo(mark, CV_8U); bitwise_not(mark, mark); -// imshow("Markers_v2", mark); // uncomment this if you want to see how the mark - // image looks like at that point + // 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); + int b = theRNG().uniform(0, 256); + int g = theRNG().uniform(0, 256); + int r = theRNG().uniform(0, 256); colors.push_back(Vec3b((uchar)b, (uchar)g, (uchar)r)); } @@ -155,16 +158,16 @@ int main() { 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); -//! [watershed] + //! [watershed] - waitKey(0); + waitKey(); return 0; } |