OpenCV SVM train()がうまく機能しない

前提・実現したいこと

OpenCVでSVMを利用するため，サンプルコードを実行して動作を勉強したいと考えています．

発生している問題・エラーメッセージ

古いコードだったので色々修正を加えて動作する段階までこぎつけたものの，train()の部分がうまく動作してくれません．
分類結果の出力の形式に問題があると言っているように見えるのですが，具体的にどこを修正すれば良いかがわかりませんでした．

OpenCV(4.5.3) Error: Bad argument (in the case of classification problem the responses must be categorical; either specify varType when creating TrainData, or pass integer responses) in cv::ml::SVMImpl::train, file C:\build\master_winpack-build-win64-vc15\opencv\modules\ml\src\svm.cpp, line 1630
OpenCV: terminate handler is called! The last OpenCV error is:
OpenCV(4.5.3) Error: Bad argument (in the case of classification problem the responses must be categorical; either specify varType when creating TrainData, or pass integer responses) in cv::ml::SVMImpl::train, file C:\build\master_winpack-build-win64-vc15\opencv\modules\ml\src\svm.cpp, line 1630

該当のソースコード

Cpp
1#include <iostream>
2#include <fstream>
3#include <opencv2/opencv.hpp>
4#include <opencv2/core/core.hpp>
5#include <opencv2/features2d/features2d.hpp>
6#include <opencv2/highgui/highgui.hpp>
7#include <opencv2/xfeatures2d/nonfree.hpp>
8#include <opencv2/imgcodecs.hpp>
9#include <opencv2/ml/ml.hpp>
10#include <stdio.h>
11#include <stdlib.h>
12#include <fstream>
13#include <iostream>
14
15
16#pragma warning(disable:4996)
17
18using namespace cv;
19using namespace std;
20
21const int WINDOW_HEIGHT = 512;
22const int WINDOW_WIDTH = 512;
23const int TRAINING_DATA_NUM = 200;
24
25inline int labelOf(double x, double y) {
26    // return (x - 0.5) * (x - 0.5) + (y - 0.5) * (y - 0.5) <= 0.1 ? 1 : -1;
27    // return y >= sin(10 * x) ? 1 : -1;
28    return y >= 10 * x * x ? 1 : -1;
29}
30
31int main() {
32    // Data for visual representation
33    Mat image = Mat::zeros(WINDOW_HEIGHT, WINDOW_WIDTH, CV_8UC3);
34
35    // Set up training data
36    Mat trainingData(TRAINING_DATA_NUM, 2, CV_32FC1);
37    Mat labels(TRAINING_DATA_NUM, 1, CV_32FC1);
38
39    for (int i = 0; i < TRAINING_DATA_NUM; i++) {
40        float x = 1. * rand() / RAND_MAX;
41        float y = 1. * rand() / RAND_MAX;
42
43        trainingData.at<float>(i, 0) = x;
44        trainingData.at<float>(i, 1) = y;
45
46        labels.at<float>(i, 0) = labelOf(x, y);
47    }
48
49    
50    // Set up SVM's parameters
51    Ptr<ml::SVM> params = ml::SVM::create();
52    
53    
54    params->setType(ml::SVM::C_SVC);
55    params->setKernel(ml::SVM::RBF);
56    params->setDegree(3);
57    params->setGamma(3);
58    //params->setCoef0(0);
59    params->setC(0.1);
60    //params->setP(0.1);
61    //params->setNu(0.5);
62    params->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER, 100, 1e-6));
63    
64    // Train the SVM
65    params->train(trainingData, ml::ROW_SAMPLE, labels);
66    
67
68    // Show the decision regions given by the SVM
69    Vec3b red(0, 0, 128), blue(128, 0, 0);
70    for (int i = 0; i < image.rows; ++i) {
71        for (int j = 0; j < image.cols; ++j)
72        {
73            Mat sampleMat =
74                (Mat_<float>(1, 2) << 1. * j / WINDOW_HEIGHT, 1. * i / WINDOW_WIDTH);
75            float response = params->predict(sampleMat);
76            if (response == 1)
77                image.at<Vec3b>(i, j) = red;
78            else if (response == -1)
79                image.at<Vec3b>(i, j) = blue;
80        }
81    }
82
83    // Show the training data
84    for (int i = 0; i < TRAINING_DATA_NUM; i++) {
85
86        if (labels.at<float>(i, 0) == 1)
87            circle(image, Point(trainingData.at<float>(i, 0) * WINDOW_HEIGHT, trainingData.at<float>(i, 1) * WINDOW_WIDTH), 3, Scalar(0, 0, 255), -1, 8);
88        else
89            circle(image, Point(trainingData.at<float>(i, 0) * WINDOW_HEIGHT, trainingData.at<float>(i, 1) * WINDOW_WIDTH), 3, Scalar(255, 0, 0), -1, 8);
90    }
91
92    // Show support vectors
93    imshow("Kernel SVM", image);
94
95    waitKey(0);
96}