opencv 学习之 SVM / ANN 图片分类（OPENCV3.2）

#include <string>
#include <iostream>
#include <vector>

#include <opencv2/core.hpp>
#include <opencv2/imgproc.hpp>
#include "opencv2/imgcodecs.hpp"
#include <opencv2/highgui.hpp>
#include <opencv2/ml.hpp>
#include<opencv2/opencv.hpp>

using namespace std;
using namespace cv;
using namespace cv::ml;

int SVM_TEST()
{
//训练2组数据，每组4帧
int labels[8] = { -1, -1, -1, -1, 1, 1, 1, 1 };
Mat labelsMat(8, 1, CV_32SC1, labels);

//样本宽高
int smpW = 540;
int smpH = 360;

//将样本的值逐像素赋给训练图像
Mat trainMat(8, smpW * smpH, CV_32FC1);
for (int i = 0; i < 2; i++)
{
for (int j = 1; j < 5; j++)
{
string path = format("E:\\video\\tmp\\Img\\%d%d.bmp", i, j);
Mat img = imread(path, 0);
//Canny(img, img, 50, 100);

int index1 = i * 4 + j - 1;
float * trainData = trainMat.ptr<float>(index1);

for (int h = 0; h < smpH; h++)
{
uchar * imgData = img.ptr<uchar>(h);
for (int w = 0; w < smpW; w++)
{
int index2 = h*smpW + w;
trainData[index2] = imgData[w];
}
}
}
}

//Ptr<SVM> svm = SVM::create();
//svm->setType(SVM::C_SVC);
//svm->setKernel(SVM::LINEAR);
//svm->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER, 100, 1e-6));

////设置训练数据
//Ptr<TrainData> tData = TrainData::create(trainMat, ROW_SAMPLE, labelsMat);
//svm->train(tData);
//svm->save("d:\\data.xml");

Ptr<SVM> svm = SVM::load("d:\\data.xml");

for (int i = 0; i < 2; i++)
{
for (int j = 5; j < 6; j++)
{
Mat testMat(1, smpW * smpH, CV_32FC1);
float * testData = testMat.ptr<float>(0);

string path = format("E:\\video\\tmp\\Img\\%d%d.bmp", i, j);
Mat img = imread(path, 0);
//Canny(img, img, 50, 100);

for (int h = 0; h < smpH; h++)
{
uchar * imgData = img.ptr<uchar>(h);
for (int w = 0; w < smpW; w++)
{
int index2 = h*smpW + w;
testData[index2] = imgData[w] * 1.0;
}
}

Mat ret(1, 1, CV_32S);
float response = svm->predict(testMat, ret);  //进行预测，返回1或-1
cout << response << endl;
float * data = ret.ptr<float>(0);
cout << data[0] << endl;
waitKey(10);
}
}
return 0;
}

int ANN_TEST()
{
// 540 x 360 矩阵
int smpW = 540;
int smpH = 360;

float labels[8] = { 0,0,0,0,1,1,1,1 };  // 每个样本数据对应的输出
Mat labelsMat(8, 1, CV_32FC1, labels);
cout << labelsMat << endl;

// 训练样本
Mat trainMat(8, smpW * smpH, CV_32FC1);
for (int i = 0; i < 2; i++)
{
for (int j = 1; j < 5; j++)
{
string path = format("E:\\video\\tmp\\Img\\%d%d.bmp", i, j);
Mat img = imread(path, 0);
if (img.empty())
{
return 0;
}
//Canny(img, img, 50, 100);

int index1 = i * 4 + j - 1;
float * trainData = trainMat.ptr<float>(index1);

for (int h = 0; h < smpH; h++)
{
uchar * imgData = img.ptr<uchar>(h);
for (int w = 0; w < smpW; w++)
{
int index2 = h*smpW + w;
trainData[index2] = imgData[w];
}
}
}
}

//// BP 模型创建和参数设置
//Ptr<ANN_MLP> bp = ANN_MLP::create();

//Mat layerSizes = (Mat_<int>(1, 3) << smpW * smpH, 6, 1);
//bp->setLayerSizes(layerSizes);

//bp->setTrainMethod(ANN_MLP::BACKPROP, 0.1, 0.1);
//bp->setActivationFunction(ANN_MLP::SIGMOID_SYM);
//bp->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER, 10000, /*FLT_EPSILON*/1e-6));

//// 保存训练好的神经网络参数
//bool trained = bp->train(trainMat, ROW_SAMPLE, labelsMat);
//if (trained)
//{
//	bp->save("d:\\bp_param.xml");
//}

// 创建训练好的神经网络
Ptr<ANN_MLP> bp = ANN_MLP::load("d:\\bp_param.xml");

for (int i = 0; i < 2; i++)
{
for (int j = 5; j < 6; j++)
{
Mat testMat(1, smpW * smpH, CV_32FC1);
float * testData = testMat.ptr<float>(0);

string path = format("E:\\video\\tmp\\Img\\%d%d.bmp", i, j);
Mat img = imread(path, 0);
//Canny(img, img, 50, 100);

for (int h = 0; h < smpH; h++)
{
uchar * imgData = img.ptr<uchar>(h);
for (int w = 0; w < smpW; w++)
{
int index2 = h*smpW + w;
testData[index2] = imgData[w] * 1.0;
}
}

Mat responseMat;
bp->predict(testMat, responseMat);
float response = responseMat.ptr<float>(0)[0];
cout << response << endl;
float * data = responseMat.ptr<float>(0);
cout << data[0] << endl;
waitKey(10);
}
}

waitKey(0);
return 0;
}

int main()
{
SVM_TEST();
ANN_TEST();
}