机器学习---opencv实现简单的KNN算法

注意：我的OpenCV的版本是3.0.0，可能是版本的原因吧，从网上找的测试程序一直出错。一个简单的Demo例子。

#include<opencv2/opencv.hpp>
#include <opencv2/ml/ml.hpp>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include<iostream>
#include "ml.hpp"
#include "highgui.h"
#include <stdlib.h>
#include <time.h>

using namespace cv;
using namespace std;
using namespace cv::ml;

int main()
{
float labels[10] = { 0, 0, 0, 0, 0, 1, 1, 1, 1, 1 };
Mat labelsMat(10, 1, CV_32FC1, labels);
cout << labelsMat << endl;
cout << labelsMat.size() << endl;
float trainingData[10][2];
int k = 5;
srand(time(0));
for (int i = 0; i<5; i++){
trainingData[i][0] = rand() % 255 + 1;
trainingData[i][1] = rand() % 255 + 1;
trainingData[i + 5][0] = rand() % 255 + 255;
trainingData[i + 5][1] = rand() % 255 + 255;
}
Mat trainingDataMat(10, 2, CV_32FC1, trainingData);
cout << trainingDataMat << endl;

KNearest::Params params;
params.defaultK = 5;
params.isclassifier = true;

Ptr<TrainData> knn;
knn = TrainData::create(trainingDataMat, ROW_SAMPLE, labelsMat);
Ptr<KNearest> knn1;
knn1 = StatModel::train<KNearest>(knn, params);

int width = 512, height = 512;
Mat image = Mat::zeros(height, width, CV_8UC3);
Mat testlabelMat1 = Mat::zeros(1,height*width, CV_8UC3);
Vec3b green(0, 255, 0), blue(255, 0, 0);

for (int i = 0; i < image.rows; ++i)
{
for (int j = 0; j < image.cols; ++j)
{
const Mat sampleMat = (Mat_<float>(1, 2) << i, j);

float r = knn1->predict(sampleMat);
if (r != 0){
image.at<Vec3b>(j, i) = green;
}
else
image.at<Vec3b>(j, i) = blue;
}
}
for (int i = 0; i<5; i++){
circle(image, Point(trainingData[i][0], trainingData[i][1]),
5, Scalar(0, 0, 0), -1, 8);
circle(image, Point(trainingData[i + 5][0], trainingData[i + 5][1]),
5, Scalar(255, 255, 255), -1, 8);
}
imshow("KNN Simple Example", image);
waitKey(10000);

waitKey(0);


Demo 的结果：