Kmeans

double cv::kmeans(InputArray dataint k  InputOutputArray bestLabels //输出的所有样本的标签数组TermCriteria criteriaint attempts //采样不同初始化标签的尝试次数int flag // 中心点初始化方法，支持KMEANS_RANDOM_CENTERS//KMEANS_PP_CENTERS//KMEANS_USE_INITIAL_LABELSOutputArray centers=noArray()
)

kmeans实现图像语义分割示例代码：

void MLoperatorsDemo::kmeans_segmentation_demo(Mat &image) {Scalar colorTab[] = {Scalar(0, 0, 255),Scalar(0, 255, 0),Scalar(255, 0, 0),Scalar(0, 255, 255),Scalar(255, 0, 255)};int width = image.cols;int height = image.rows;int dims = image.channels();// 初始化定义int sampleCount = width*height;int clusterCount = 5;Mat labels;Mat centers;// RGB 数据转换到样本数据Mat sample_data = image.reshape(3, sampleCount);Mat data;sample_data.convertTo(data, CV_32F);// 运行K-MeansTermCriteria criteria = TermCriteria(TermCriteria::EPS + TermCriteria::COUNT, 10, 0.1);kmeans(data, clusterCount, labels, criteria, clusterCount, KMEANS_PP_CENTERS, centers);// 显示图像分割结果int index = 0;Mat result = Mat::zeros(image.size(), image.type());for (int row = 0; row < height; row++) {for (int col = 0; col < width; col++) {index = row*width + col;int label = labels.at<int>(index, 0);result.at<Vec3b>(row, col)[0] = colorTab[label][0];result.at<Vec3b>(row, col)[1] = colorTab[label][1];result.at<Vec3b>(row, col)[2] = colorTab[label][2];}}imshow("KMeans图像分割", result);
}

KNN分类

KNN实现手写数字识别：

void MLoperatorsDemo::knn_digit_train(Mat &image) {Mat gray;cvtColor(image, gray, COLOR_BGR2GRAY);// 分割为5000个cellsMat images = Mat::zeros(5000, 400, CV_8UC1);Mat labels = Mat::zeros(5000, 1, CV_8UC1);int index = 0;Rect roi;roi.x = 0;roi.height = 1;roi.width = 400;for (int row = 0; row < 50; row++) {int label = row / 5;int offsety = row * 20;for (int col = 0; col < 100; col++) {int offsetx = col * 20;Mat digit = Mat::zeros(Size(20, 20), CV_8UC1);for (int sr = 0; sr < 20; sr++) {for (int sc = 0; sc < 20; sc++) {digit.at<uchar>(sr, sc) = gray.at<uchar>(sr + offsety, sc + offsetx);}}Mat one_row = digit.reshape(1, 1);roi.y = index;one_row.copyTo(images(roi));labels.at<uchar>(index, 0) = label;index++;}}printf("load sample hand-writing data...\n");// 转换为浮点数images.convertTo(images, CV_32FC1);labels.convertTo(labels, CV_32SC1);printf("load sample hand-writing data...\n");// 开始KNN训练printf("Start to knn train...\n");Ptr<ml::KNearest> knn = ml::KNearest::create();knn->setDefaultK(5);knn->setIsClassifier(true);Ptr<ml::TrainData> tdata = ml::TrainData::create(images, ml::ROW_SAMPLE, labels);knn->train(tdata);knn->save("D:/vcworkspaces/knn_knowledge.yml");printf("Finished KNN...\n");
}void MLoperatorsDemo::knn_digit_test() {// real test itMat t1 = imread(rootdir + "knn_01.png", IMREAD_GRAYSCALE);Mat t2 = imread(rootdir + "knn_02.png", IMREAD_GRAYSCALE);namedWindow("t1", WINDOW_FREERATIO);namedWindow("t2", WINDOW_FREERATIO);imshow("t1", t1);imshow("t2", t2);Mat m1, m2;resize(t1, m1, Size(20, 20));resize(t2, m2, Size(20, 20));Mat testdata = Mat::zeros(2, 400, CV_8UC1);Mat testlabels = Mat::zeros(2, 1, CV_32SC1);Rect rect;rect.x = 0;rect.y = 0;rect.height = 1;rect.width = 400;Mat one = m1.reshape(1, 1);Mat two = m2.reshape(1, 1);one.copyTo(testdata(rect));rect.y = 1;two.copyTo(testdata(rect));testlabels.at<int>(0, 0) = 1;testlabels.at<int>(1, 0) = 2;testdata.convertTo(testdata, CV_32F);// 加载KNN分类器Ptr<ml::KNearest> knn = Algorithm::load<ml::KNearest>("D:/vcworkspaces/knn_knowledge.yml");Mat result;knn->findNearest(testdata, 5, result);for (int i = 0; i< result.rows; i++) {int predict = result.at<float>(i, 0);printf("knn t%d predict : %d, actual label ：%d \n", (i + 1), predict, testlabels.at<int>(i, 0));}
}

SVM与HOG实现对象检测：

void MLoperatorsDemo::get_hog_descriptor(Mat &image, vector<float> &desc) {HOGDescriptor hog;int h = image.rows;int w = image.cols;float rate = 64.0 / w;Mat img, gray;resize(image, img, Size(64, int(rate*h)));  //宽设置为64且图像的宽高比不变cvtColor(img, gray, COLOR_BGR2GRAY);Mat result = Mat::zeros(Size(64, 128), CV_8UC1);result = Scalar(127);Rect roi;roi.x = 0;roi.width = 64;roi.y = (128 - gray.rows) / 2;roi.height = gray.rows;gray.copyTo(result(roi)); //图像resize之后对缺失部分进行灰度填充，所以hog.compute不能直接用gray（宽高比不对）hog.compute(result, desc, Size(8, 8), Size(0, 0));
}void MLoperatorsDemo::train_ele_watch(std::string positive_dir, std::string negative_dir) {// 创建变量Mat trainData = Mat::zeros(Size(3780, 26), CV_32FC1);Mat labels = Mat::zeros(Size(1, 26), CV_32SC1);vector<string> images;glob(positive_dir, images);int pos_num = images.size();// 生成正负样本数据for (int i = 0; i < images.size(); i++) {Mat image = imread(images[i].c_str());vector<float> fv;get_hog_descriptor(image, fv);printf("image path : %s, feature data length: %d \n", images[i].c_str(), fv.size());for (int j = 0; j < fv.size(); j++) {trainData.at<float>(i, j) = fv[j];}labels.at<int>(i, 0) = 1;}images.clear();glob(negative_dir, images);for (int i = 0; i < images.size(); i++) {Mat image = imread(images[i].c_str());vector<float> fv;get_hog_descriptor(image, fv);printf("image path : %s, feature data length: %d \n", images[i].c_str(), fv.size());for (int j = 0; j < fv.size(); j++) {trainData.at<float>(i + pos_num, j) = fv[j];}labels.at<int>(i + pos_num, 0) = -1;}// 训练SVM仪表分类器printf("\n start SVM training... \n");Ptr< ml::SVM > svm = ml::SVM::create();svm->setKernel(ml::SVM::LINEAR);svm->setC(2.0);svm->setType(ml::SVM::C_SVC);svm->train(trainData, ml::ROW_SAMPLE, labels);clog << "...[done]" << endl;// save xmlsvm->save("D:/vcworkspaces/svm_hog_elec.yml");
}void MLoperatorsDemo::hog_svm_detector_demo(Mat &image) {// 创建HOG与加载SVM训练数据HOGDescriptor hog;Ptr<ml::SVM> svm = ml::SVM::load("D:/vcworkspaces/svm_hog_elec.yml");Mat sv = svm->getSupportVectors();Mat alpha, svidx;double rho = svm->getDecisionFunction(0, alpha, svidx);// 构建detectorvector<float> svmDetector;svmDetector.clear();svmDetector.resize(sv.cols + 1);for (int j = 0; j < sv.cols; j++) {//sv.cols=3780=fv.sizesvmDetector[j] = -sv.at<float>(0, j);}svmDetector[sv.cols] = (float)rho;hog.setSVMDetector(svmDetector);vector<Rect> objects;hog.detectMultiScale(image, objects, 0.1, Size(8, 8), Size(32, 32), 1.25);for (int i = 0; i < objects.size(); i++) {rectangle(image, objects[i], Scalar(0, 0, 255), 2, 8, 0);}namedWindow("SVM+HOG对象检测演示", WINDOW_FREERATIO);imshow("SVM+HOG对象检测演示", image);
}