Image Segmentation .cpp tutorial

Distance Transform tutorial fixes

Distance Transform fixes v.2

Distance Transform fixes v.3

Distance Transform fixes v.4

samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp

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+/**
+ * @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
+ */
+
+#include <opencv2/opencv.hpp>
+#include <iostream>
+
+using namespace std;
+using namespace cv;
+
+int main(int, char** argv)
+{
+//! [load_image]
+    // 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);
+//! [load_image]
+
+//! [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;
+          }
+        }
+    }
+
+    // Show output image
+    imshow("Black Background Image", src);
+//! [black_bg]
+
+//! [sharp]
+    // 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 );
+//! [sharp]
+
+    src = imgResult; // copy back
+
+//! [bin]
+    // 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);
+//! [bin]
+
+//! [dist]
+    // 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);
+//! [dist]
+
+//! [peaks]
+    // 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);
+//! [peaks]
+
+//! [seeds]
+    // 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);
+//! [seeds]
+
+//! [watershed]
+    // 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);
+//! [watershed]
+
+    waitKey(0);
+    return 0;
+}