利用opencv3.4.1進行隨機森林的分類

#include"pch.h"
#include<opencv2/opencv.hpp>
#include<opencv2/ml/ml.hpp>

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

int main()
{
	const int Kwidth = 512;
	const int Kheight = 512;

	//用於顯示分類結果的圖像
	Mat image = Mat::zeros(Kheight, Kwidth, CV_8UC3);

	//組織分類標籤,三類，每類50個樣本
	int labels[150];
	for (int i = 0; i < 50; i++)
		labels[i] = 1;
	for (int i = 50; i < 100; i++)
		labels[i] = 2;
	for (int i = 100; i < 150; i++)
		labels[i] = 3;
	Mat labelsMat(150, 1, CV_32SC1, labels);  //矩陣化,150*1的矩陣

	//組織訓練數據，三類數據，每個數據點爲二維特徵向量
	float trainDataArray[150][2];
	RNG rng;
	for (int i = 0; i < 50; i++)
	{
		trainDataArray[i][0] = 250 + static_cast<float>(rng.gaussian(30));
		trainDataArray[i][1] = 250 + static_cast<float>(rng.gaussian(30));
	}
	for (int i = 50; i < 100; i++)
	{
		trainDataArray[i][0] = 150 + static_cast<float>(rng.gaussian(30));
		trainDataArray[i][1] = 150 + static_cast<float>(rng.gaussian(30));
	}
	for (int i = 100; i < 150; i++)
	{
		trainDataArray[i][0] = 320 + static_cast<float>(rng.gaussian(30));
		trainDataArray[i][1] = 150 + static_cast<float>(rng.gaussian(30));
	}
	Mat trainingDataMat(150, 2, CV_32FC1, trainDataArray); //矩陣化,150*2的矩陣

	// 設置訓練數據，把特徵數據和標籤數據放在一起
	Ptr<TrainData> tData = TrainData::create(trainingDataMat, ROW_SAMPLE, labelsMat);

	//創建分類器，並設置訓練參數
	Ptr<RTrees> rtrees = RTrees::create();
	rtrees->setMaxDepth(10);
	rtrees->setMinSampleCount(10);
	rtrees->setRegressionAccuracy(0);
	rtrees->setUseSurrogates(false);
	rtrees->setMaxCategories(15);
	rtrees->setPriors(Mat());
	rtrees->setCalculateVarImportance(true);
	rtrees->setActiveVarCount(4);
	rtrees->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER + (0.01f > 0 ? TermCriteria::EPS : 0), 100, 0.01f));
	//開始訓練模型
	rtrees->train(tData);

	//對圖像內所有512*512個背景點進行預測，不同的預測結果，圖像背景區域顯示不同的顏色
	Vec3b red(0, 0, 255), green(0, 255, 0), blue(255, 0, 0);
	for (int i = 0; i < image.rows; ++i)
		for (int j = 0; j < image.cols; ++j)
		{
			Mat sampleMat = (Mat_<float>(1, 2) << j, i);  //生成測試數據
			float response = rtrees->predict(sampleMat);  //進行預測，返回1或-1
			if (response == 1)
				image.at<Vec3b>(i, j) = red;
			else if (response == 2)
				image.at<Vec3b>(i, j) = green;
			else
				image.at<Vec3b>(i, j) = blue;
		}

	//把訓練樣本點，顯示在圖相框內
	for (int i = 0; i < trainingDataMat.rows; i++)
	{
		const float * v = trainingDataMat.ptr<float>(i);
		Point pt = Point((int)v[0], (int)v[1]);
		if (labels[i] == 1) //不同的圓點，標記不同的顏色
			circle(image, pt, 5, Scalar::all(0), -1, 8);
		else if (labels[i] == 2)
			circle(image, pt, 5, Scalar::all(128), -1, 8);
		else
			circle(image, pt, 5, Scalar::all(255), -1, 8);
	}

	//顯示分類結果圖像
	imshow("隨機森林分類器示例", image);
	waitKey(0);
	return 0;
}

執行後的結果如下：