使用opencv里面的神经网络

参考资料
http://blog.csdn.net/xiaowei_cqu/article/details/9027617
这几天做人脸姿态检测的时候需要用的姿态的分类，由于之前了解过bp神经网络，所以就使用神经网络对姿态进行分类，

问题：

知道左眼和右眼的坐标、鼻子的坐标，如何经过训练来得知脸部姿态旋转了多少度？

解决方法：

使用bp神经网络进行训练；

准备训练样本（这个还没有准备充分，下节接着讨论）。

源程序：

#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][1] = { { -30}, {0 }, { 30 } };
Mat labelsMat(3, 1, CV_32FC1, labels);

float trainingData[3][3] = { { 244, 152, 213 }, { 244, 152, 198 }, { 244, 152, 165 } };
Mat trainingDataMat(3, 3, CV_32FC1, trainingData);
Mat layerSizes = (Mat_<int>(1, 5) << 3, 2, 2, 2, 1);
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, 3) << i, j, 0);
Mat responseMat;
bp.predict(sampleMat, responseMat);
float* p = responseMat.ptr<float>(0);
//float response = 0.0f;
//for (int k = 0; k<3; k++){
//	//	cout<<p[k]<<" ";
//	response += p[k];
//}
if (p[0] >0)
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);

}