opencv2笔记04-人工神经网络（ANN）

关于单个神经元的感知机和最基本的BP神经网络的模型，以及它们的推导过程，已经在之前的博文给出。点击打开链接 点击打开链接

这里参考CSDN上的博客，利用opencv接口，实现了神经网络的train和predict过程。

代码实现：

int cv_ann()
{
//Setup the BPNetwork
CvANN_MLP bp;
// Set up BPNetwork's parameters
CvANN_MLP_TrainParams params;
params.train_method=CvANN_MLP_TrainParams::BACKPROP;  //(Back Propagation,BP)反向传播算法
params.bp_dw_scale=0.1;
params.bp_moment_scale=0.1;

// Set up training data
float labels[10][2] = {{0.9,0.1},{0.1,0.9},{0.9,0.1},{0.1,0.9},{0.9,0.1},{0.9,0.1},{0.1,0.9},{0.1,0.9},{0.9,0.1},{0.9,0.1}};
//这里对于样本标记为0.1和0.9而非0和1，主要是考虑到sigmoid函数的输出为一般为0和1之间的数，只有在输入趋近于-∞和+∞才逐渐趋近于0和1，而不可能达到。
Mat labelsMat(10, 2, CV_32FC1, labels);

float trainingData[10][2] = { {11,12},{111,112}, {21,22}, {211,212},{51,32}, {71,42}, {441,412},{311,312}, {41,62}, {81,52} };
Mat trainingDataMat(10, 2, CV_32FC1, trainingData);
Mat layerSizes=(Mat_<int>(1,5) << 2, 2, 2, 2, 2); //5层：输入层，3层隐藏层和输出层，每层均为两个perceptron
bp.create(layerSizes,CvANN_MLP::SIGMOID_SYM);//CvANN_MLP::SIGMOID_SYM ,选用sigmoid作为激励函数
bp.train(trainingDataMat, labelsMat, Mat(),Mat(), params);  //训练

// 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
for (int i = 0; i < image.rows; ++i)
{
for (int j = 0; j < image.cols; ++j)
{
Mat sampleMat = (Mat_<float>(1,2) << i,j);
Mat responseMat;
bp.predict(sampleMat,responseMat);
float* p=responseMat.ptr<float>(0);
//
if (p[0] > p[1])
{
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(111,  112), 5, Scalar(  0,   0,   0), thickness, lineType);
circle( image, Point(211,  212), 5, Scalar(  0,   0,   0), thickness, lineType);
circle( image, Point(441,  412), 5, Scalar(  0,   0,   0), thickness, lineType);
circle( image, Point(311,  312), 5, Scalar(  0,   0,   0), thickness, lineType);
circle( image, Point(11,  12), 5, Scalar(255, 255, 255), thickness, lineType);
circle( image, Point(21, 22), 5, Scalar(255, 255, 255), thickness, lineType);
circle( image, Point(51,  32), 5, Scalar(255, 255, 255), thickness, lineType);
circle( image, Point(71, 42), 5, Scalar(255, 255, 255), thickness, lineType);
circle( image, Point(41,  62), 5, Scalar(255, 255, 255), thickness, lineType);
circle( image, Point(81, 52), 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);
return 0;
}

效果图：



Reference：

[1] http://blog.csdn.net/xiaowei_cqu/article/details/9027617