【轉】OpenCV中使用神經網絡 CvANN_MLP

原文見：http://blog.csdn.net/xiaowei_cqu/article/details/9027617

OpenCV的ml模塊實現了人工神經網絡（Artificial Neural Networks， ANN）最典型的多層感知器（multi-layer perceptrons, MLP）模型。由於ml模型實現的算法都繼承自統一的CvStatModel基類，其訓練和預測的接口都是train(),predict()，非常簡單。

下面來看神經網絡 CvANN_MLP 的使用~

定義神經網絡及參數：

 

//Setup the BPNetwork
	CvANN_MLP bp; 
	// Set up BPNetwork's parameters
	CvANN_MLP_TrainParams params;
	params.train_method=CvANN_MLP_TrainParams::BACKPROP;
	params.bp_dw_scale=0.1;
	params.bp_moment_scale=0.1;
	//params.train_method=CvANN_MLP_TrainParams::RPROP;
	//params.rp_dw0 = 0.1; 
	//params.rp_dw_plus = 1.2; 
	//params.rp_dw_minus = 0.5;
	//params.rp_dw_min = FLT_EPSILON; 
	//params.rp_dw_max = 50.; 

可以直接定義CvANN_MLP神經網絡，並設置其參數。 BACKPROP表示使用back-propagation的訓練方法，RPROP即最簡單的propagation訓練方法。

使用BACKPROP有兩個相關參數：bp_dw_scale即bp_moment_scale：

使用PRPOP有四個相關參數：rp_dw0, rp_dw_plus, rp_dw_minus, rp_dw_min, rp_dw_max：

上述代碼中為其默認值。

設置網絡層數，訓練數據：

 

// Set up training data
	float labels[3][5] = {{0,0,0,0,0},{1,1,1,1,1},{0,0,0,0,0}};
	Mat labelsMat(3, 5, CV_32FC1, labels);

	float trainingData[3][5] = { {1,2,3,4,5},{111,112,113,114,115}, {21,22,23,24,25} };
	Mat trainingDataMat(3, 5, CV_32FC1, trainingData);
	Mat layerSizes=(Mat_<int>(1,5) << 5,2,2,2,5);
	bp.create(layerSizes,CvANN_MLP::SIGMOID_SYM);//CvANN_MLP::SIGMOID_SYM
	                                           //CvANN_MLP::GAUSSIAN
	                                           //CvANN_MLP::IDENTITY
	bp.train(trainingDataMat, labelsMat, Mat(),Mat(), params);

　　

layerSizes設置了有三個隱含層的網絡結構：輸入層，三個隱含層，輸出層。輸入層和輸出層節點數均為5，中間隱含層每層有兩個節點。

 

create第二個參數可以設置每個神經節點的激活函數，默認為CvANN_MLP::SIGMOID_SYM，即Sigmoid函數，同時提供的其他激活函數有Gauss和階躍函數。

使用訓練好的網絡結構分類新的數據：

然后直接使用predict函數，就可以預測新的節點：

 

Mat sampleMat = (Mat_<float>(1,5) << i,j,0,0,0);
			Mat responseMat;
			bp.predict(sampleMat,responseMat);

　　

 

 

完整程序代碼：

//The example of using BPNetwork in OpenCV
//Coded by L. Wei
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/ml/ml.hpp>
#include <iostream>
#include <string>

using namespace std;
using namespace cv;

int main()
{
	//Setup the BPNetwork
	CvANN_MLP bp; 
	// Set up BPNetwork's parameters
	CvANN_MLP_TrainParams params;
	params.train_method=CvANN_MLP_TrainParams::BACKPROP;
	params.bp_dw_scale=0.1;
	params.bp_moment_scale=0.1;
	//params.train_method=CvANN_MLP_TrainParams::RPROP;
	//params.rp_dw0 = 0.1; 
	//params.rp_dw_plus = 1.2; 
	//params.rp_dw_minus = 0.5;
	//params.rp_dw_min = FLT_EPSILON; 
	//params.rp_dw_max = 50.;

	// Set up training data
	float labels[3][5] = {{0,0,0,0,0},{1,1,1,1,1},{0,0,0,0,0}};
	Mat labelsMat(3, 5, CV_32FC1, labels);

	float trainingData[3][5] = { {1,2,3,4,5},{111,112,113,114,115}, {21,22,23,24,25} };
	Mat trainingDataMat(3, 5, CV_32FC1, trainingData);
	Mat layerSizes=(Mat_<int>(1,5) << 5,2,2,2,5);
	bp.create(layerSizes,CvANN_MLP::SIGMOID_SYM);//CvANN_MLP::SIGMOID_SYM
	                                           //CvANN_MLP::GAUSSIAN
	                                           //CvANN_MLP::IDENTITY
	bp.train(trainingDataMat, labelsMat, Mat(),Mat(), params);


	// Data for visual representation
	int width = 512, height = 512;
	Mat image = Mat::zeros(height, width, CV_8UC3);
	Vec3b green(0,255,0), blue (255,0,0);
	// Show the decision regions given by the SVM
	for (int i = 0; i < image.rows; ++i)
		for (int j = 0; j < image.cols; ++j)
		{
			Mat sampleMat = (Mat_<float>(1,5) << i,j,0,0,0);
			Mat responseMat;
			bp.predict(sampleMat,responseMat);
			float* p=responseMat.ptr<float>(0);
			int response=0;
			for(int i=0;i<5;i++){
			//	cout<<p[i]<<" ";
				response+=p[i];
			}
			if (response >2)
				image.at<Vec3b>(j, i)  = green;
			else  
				image.at<Vec3b>(j, i)  = blue;
		}

		// Show the training data
		int thickness = -1;
		int lineType = 8;
		circle(	image, Point(501,  10), 5, Scalar(  0,   0,   0), thickness, lineType);
		circle(	image, Point(255,  10), 5, Scalar(255, 255, 255), thickness, lineType);
		circle(	image, Point(501, 255), 5, Scalar(255, 255, 255), thickness, lineType);
		circle(	image, Point( 10, 501), 5, Scalar(255, 255, 255), thickness, lineType);

		imwrite("result.png", image);        // save the image 

		imshow("BP Simple Example", image); // show it to the user
		waitKey(0);

}