Opencv3神经网络的使用

来源：互联网发布：vivo手机维修软件编辑：程序博客网时间：2024/06/05 06:05

Ann神经网络，看网上没搜到相关的博客，所有参照以前旧版的ANN改写了下新版的ANN使用方法

参考了博客，把2.4版本的旧版cvAnn改成了新版的ml::ANN_MLP，如果有问题欢迎留言交流~

结果如下：

下面就是简单粗暴的代码了：

//编程环境：VS2015 + Opencv3.1#include<opencv2/opencv.hpp>#include <iostream>  #include <string>  using namespace std;using namespace cv;using namespace ml;int main(){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perceptronPtr<ANN_MLP> ann = ANN_MLP::create();ann->setLayerSizes(layerSizes);//ann->setActivationFunction(ANN_MLP::SIGMOID_SYM);//ann->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 300, FLT_EPSILON));ann->setTrainMethod(ANN_MLP::BACKPROP,0.1,0.9);Ptr<TrainData> tData = TrainData::create(trainingDataMat, ROW_SAMPLE, labelsMat);ann->train(tData); // 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);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;ann->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;}

祝机器学习愉快~

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