opecv训练数字 0， 2（简单分类）
本文共 5605 字，大约阅读时间需要 18 分钟。
#include "stdafx.h"#include//#include //#include #include #include using namespace cv;using namespace std;#define CLASS_NUM 10#define MAT_WEIGHT 16#define MAT_HEIGHT 32#define uchar unsigned charvoid getFeature(Mat &X_train, Mat temp_img, int nIndex){ cvtColor(temp_img, temp_img, CV_BGR2GRAY); //判断图像是否加载成功 if (temp_img.empty()) { cout << "图像加载失败" << endl; return ; } for (size_t i = 0; i < MAT_HEIGHT; i++) { for (size_t j = 0; j < MAT_WEIGHT; j++) { X_train.at (nIndex, i*MAT_WEIGHT + j) = (float)temp_img.at (i, j)/255; } } } int main() { int in_layer = 512; int out_layer = 2; int SAMPLES = 10; // 训练样本总量 Mat X_train(SAMPLES, in_layer, CV_32FC1); Mat Y_train(SAMPLES, out_layer, CV_32FC1); // 构建训练数据矩阵 X_train Mat xtrain_0 = imread("data\\0\\1.jpg"); getFeature(X_train, xtrain_0, 0); xtrain_0 = imread("data\\0\\2.jpg"); getFeature(X_train, xtrain_0, 1); xtrain_0 = imread("data\\0\\3.jpg"); getFeature(X_train, xtrain_0, 2); xtrain_0 = imread("data\\0\\4.jpg"); getFeature(X_train, xtrain_0, 3); xtrain_0 = imread("data\\0\\5.jpg"); getFeature(X_train, xtrain_0, 4); Mat xtrain_1 = imread("data\\1\\1.png"); getFeature(X_train, xtrain_1, 5); xtrain_1 = imread("data\\1\\2.png"); getFeature(X_train, xtrain_1, 6); xtrain_1 = imread("data\\1\\3.png"); getFeature(X_train, xtrain_1, 7); xtrain_1 = imread("data\\1\\4.png"); getFeature(X_train, xtrain_1, 8); xtrain_1 = imread("data\\1\\5.png"); getFeature(X_train, xtrain_1, 9); //string strPath = "data\\0\\"; //for (int i = 0; i < 21; i++) //{ // string strPicPath = strPath + to_string(i) + ".jpg"; // Mat mat = imread(strPicPath); // getFeature(X_train, mat,i); //} // 构建训练标签矩阵 Y_train for (int i = 0; i < SAMPLES; i++) { Y_train.at (i, 0) = 1; Y_train.at (i, 1) = 0; if (i > SAMPLES / 2 || i == SAMPLES / 2) { Y_train.at (i, 0) = 0; Y_train.at (i, 1) = 1; } } CvANN_MLP_TrainParams params( cvTermCriteria(CV_TERMCRIT_ITER + CV_TERMCRIT_EPS, 300, 0.000001), CvANN_MLP_TrainParams::BACKPROP, 0.1, 0.1); int n_W = MAT_WEIGHT * MAT_HEIGHT; Mat layers = (Mat_ (3, 1) << n_W, 24, 2); CvANN_MLP net(layers, CvANN_MLP::SIGMOID_SYM, 0.6667f, 1.7159f); net.train(X_train, Y_train, Mat(), Mat(), params); net.save("net.xml"); int numTest = 3; Mat X_test(numTest, in_layer, CV_32FC1); Mat Y_test(numTest, out_layer, CV_32FC1); Mat testImg0 = imread("data\\0\\6.jpg"); cvtColor(testImg0, testImg0, CV_BGR2GRAY); Mat testImg1 = imread("data\\1\\6.png"); cvtColor(testImg1, testImg1, CV_BGR2GRAY); Mat testImg2 = imread("data\\1\\7.png"); cvtColor(testImg2, testImg2, CV_BGR2GRAY); for (size_t i = 0; i < MAT_HEIGHT; i++) { for (size_t j = 0; j < MAT_WEIGHT; j++) { X_test.at (0, i*MAT_WEIGHT + j) = (float)testImg0.at (i, j) / 255; X_test.at (1, i*MAT_WEIGHT + j) = (float)testImg1.at (i, j) / 255; X_test.at (2, i*MAT_WEIGHT + j) = (float)testImg2.at (i, j) / 255; } } Mat predictions(Y_test.size(), CV_32F); net.predict(X_test, predictions); cout << predictions << endl; return 0;}
最后得到的分类结果还不错：
上面用的训练样本比较少，每类只有5张图片，下面采用21张图片的：
#include "stdafx.h"#include//#include //#include #include #include using namespace cv;using namespace std;#define CLASS_NUM 10#define MAT_WEIGHT 16#define MAT_HEIGHT 32#define uchar unsigned charvoid getFeature(Mat &X_train, Mat temp_img, int nIndex){ cvtColor(temp_img, temp_img, CV_BGR2GRAY); //判断图像是否加载成功 if (temp_img.empty()) { cout << "图像加载失败" << endl; return ; } for (size_t i = 0; i < MAT_HEIGHT; i++) { for (size_t j = 0; j < MAT_WEIGHT; j++) { X_train.at (nIndex, i*MAT_WEIGHT + j) = (float)temp_img.at (i, j)/255; } } } int main() { int in_layer = 512; int out_layer = 2; int SAMPLES_0 = 21; int SAMPLES_2 = 21; int SAMPLES = SAMPLES_0 + SAMPLES_2; // 训练样本总量 Mat X_train(SAMPLES, in_layer, CV_32FC1); Mat Y_train(SAMPLES, out_layer, CV_32FC1); // 构建训练数据矩阵 X_train string strPath = "data\\0\\"; for (int i = 0; i < 21; i++) { string strPicPath = strPath + to_string(i+1) + ".jpg"; Mat mat = imread(strPicPath); getFeature(X_train, mat,i); } strPath = "data\\1\\"; for (int i = 0; i < SAMPLES_2; i++) { string strPicPath = strPath + to_string(i + 1) + ".png"; Mat mat = imread(strPicPath); getFeature(X_train, mat, i + SAMPLES_0); } // 构建训练标签矩阵 Y_train for (int i = 0; i < SAMPLES; i++) { if (i < SAMPLES_0) { Y_train.at (i, 0) = 1; Y_train.at (i, 1) = 0; } if (i > SAMPLES_0 || SAMPLES_0 == i) { Y_train.at (i, 0) = 0; Y_train.at (i, 1) = 1; } } CvANN_MLP_TrainParams params( cvTermCriteria(CV_TERMCRIT_ITER + CV_TERMCRIT_EPS, 300, 0.000001), CvANN_MLP_TrainParams::BACKPROP, 0.1, 0.1); int n_W = MAT_WEIGHT * MAT_HEIGHT; Mat layers = (Mat_ (3, 1) << n_W, 24, 2); CvANN_MLP net(layers, CvANN_MLP::SIGMOID_SYM, 0.6667f, 1.7159f); net.train(X_train, Y_train, Mat(), Mat(), params); net.save("net.xml"); int numTest = 3; Mat X_test(numTest, in_layer, CV_32FC1); Mat Y_test(numTest, out_layer, CV_32FC1); Mat testImg0 = imread("data\\0\\6.jpg"); cvtColor(testImg0, testImg0, CV_BGR2GRAY); Mat testImg1 = imread("data\\1\\6.png"); cvtColor(testImg1, testImg1, CV_BGR2GRAY); Mat testImg2 = imread("data\\1\\7.png"); cvtColor(testImg2, testImg2, CV_BGR2GRAY); for (size_t i = 0; i < MAT_HEIGHT; i++) { for (size_t j = 0; j < MAT_WEIGHT; j++) { X_test.at (0, i*MAT_WEIGHT + j) = (float)testImg0.at (i, j) / 255; X_test.at (1, i*MAT_WEIGHT + j) = (float)testImg1.at (i, j) / 255; X_test.at (2, i*MAT_WEIGHT + j) = (float)testImg2.at (i, j) / 255; } } Mat predictions(Y_test.size(), CV_32F); net.predict(X_test, predictions); cout << predictions << endl; return 0;}
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