Here is a short demo of how to do a simple shape matching using the “K Nearest Neighbors” algorithm implementation that comes with OpenCV 2.4.

The process of recognition is very simple, first you need to convert some shapes to a sets of points. Then you must train and label the kNearest class with the point sets. Then you could draw some points to the screen and use the find_nearest() function to see which label does the drawn point set will match best.

Here are some code snippets for every task above:

**Convert image to a point set**

vector<cv::Point> points; ofImage im; im.loadImage("shape.jpg"); cv::Mat original; original = ofxCv::toCv(im).clone(); // 1-channel convert if(original.channels() == 3) { cv::cvtColor(original, original, CV_RGB2GRAY); } else if(original.channels() == 4) { cv::cvtColor(original, original, CV_RGBA2GRAY); } cv::Canny(original, original, 0, 100.0); cv::Mat dilateKernel(cv::Size(3,3), CV_8UC1, cv::Scalar(1)); cv::dilate(original, original, dilateKernel); vector<vector<cv::Point> > foundc; cv::findContours(original, foundc,CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE, cv::Point(0,0)); if(foundc.size() >0) { points = foundc[0]; }

**Training kNearest**

cv::KNearest *knn; cv::Mat trainingData(points.size(), 2, CV_32FC1); cv::Mat trainingClasses(points.size(), 1, CV_32FC1); // we fill the training data with the points set // if we have couple of shapes, we must fill all points into one trainingData set // and label accordingly for(int i=0;i<points.size();i++) { trainingData.at<float>(i,0) = points[i].x; trainingData.at<float>(i,1) = points[i].y; trainingClasses.at<float>(i,0) = 1; // we label the shape as "1", the next added shape point set should be labeled as "2" and etc. } // train the algorithm knn = new kNearest(trainingData, trainingClasses);

**Find Matches**

// points that are drawn on screen vector<cv::Point> drawnPoints; cv::Mat testData(drawnPoints.size(),2,CV_32FC1); for(int i=0;i<drawnPoints.size();i++) { testData.at<float>(i,0) = drawnPoints[i].x; testData.at<float>(i,0) = drawnPoints[i].y; } // found will return the label that is closer to the drawn points int found = knn->find_nearest(testData,1);

You could check out a working example at: https://github.com/kamend/kNN_ShapeMatch