#include int >>&sample_M)
{
vectorint> >v;
vector<int>tempdata;
ifstream fs;
string s;
fs.open(samplefile);
if (fs)
{
cout << "文件打开成功" << endl;
}
while (getline(fs, s))
{
istringstream is(s);
int data;
while (!is.eof())//判断文件是否到了末尾
{
is >> data;
tempdata.push_back(data);
}
v.push_back(tempdata);
tempdata.clear();
s.clear();
}
fs.close();
int rows = v.size();
int cols = v[0].size();
vectorint>>sample(cols);
for (int i = 0; i < cols; i++)
{
sample[i].resize(rows);
for (int j = 0; j < rows; j++)
{
sample[i][j] = v[j][i];
}
}
lable_M = sample[0];
sample_M.assign(sample.begin() + 1, sample.end());
}
int main()
{
GDALAllRegister();
CPLSetConfigOption("GDAL_FLLENAME_IS_UTF8", "NO");
const char* infile = "D:\\c++\\bishe\\DATA\\JJJ_LCO8_123034_20200920.tif";
const char* outfile = "D:\\c++\\bishe\\DATA\\JJJ_LCO8_123034_20200920_out.tif";
const char* sample = "xixiaoshuiti.txt";
GDALDataset* poin = (GDALDataset*)GDALOpen(infile, GA_ReadOnly);
int src_width = poin->GetRasterXSize();
int src_height = poin->GetRasterYSize();
int bands = poin->GetRasterCount();
double geo[6] = { 0 };
const char* porj = NULL;
poin->GetGeoTransform(geo);
porj = poin->GetProjectionRef();
GDALDataType datatype = poin->GetRasterBand(1)->GetRasterDataType();
//cout << datatype << endl;
unsigned short** image = (unsigned short**)malloc(sizeof(unsigned short*) * bands);
for (int i = 0; i < bands; i++)
{
image[i] = (unsigned short*)malloc(sizeof(unsigned short) *src_width * src_height);
poin->GetRasterBand(i + 1)->RasterIO(GF_Read, 0, 0, src_width, src_height, image[i], src_width, src_height, GDT_UInt16, 0, 0);
}
Mat mat_data = Mat(bands, src_width * src_height, CV_32FC1);//把整张影像转为mat格式
for (int i = 0; i < bands; i++)
{
for (int j = 0; j < src_height * src_width; j++)
{
mat_data.at<float>(i, j) = image[i][j];
}
}
Mat data;
cv::normalize(mat_data, data, 1, 0, cv::NORM_MINMAX);
vector<int>lable_M;
vectorint>>sample_M;
split(sample, lable_M, sample_M);//拆分txt文件
/*cout << sample_M.size() << endl;
cout << sample_M[0].size() << endl;
cout << lable_M.size() << endl;*/
/*for (int i = 0; i < lable_M.size(); i++)
{
cout << lable_M[i] << endl;
}*/
Mat train_labels = Mat(1, lable_M.size(), CV_32FC1);
for (int i = 0; i < lable_M.size(); i++)
{
train_labels.at<float>(i) = lable_M[i];
}
//int* lables = new int[lable_M.size()];
//memset(lables, 0, sizeof(int)*lable_M.size());
//copy(lable_M.begin(), lable_M.end(), lables);
//Mat lablemat(lable_M.size(), 1, CV_32SC1, lables);
//cout << train_labels << endl;
Mat train_images1 = Mat(sample_M.size(), sample_M[0].size(), CV_32FC1);
for (int i = 0; i < sample_M.size(); i++)
{
for (int j = 0; j < sample_M[0].size(); j++)
{
train_images1.at<float>(i, j) = sample_M[i][j];//访问冲突
}
}
Mat train_images = Mat(sample_M.size(), sample_M[0].size(), CV_32FC1);
cv::normalize(train_images1, train_images, 1, 0, cv::NORM_MINMAX);//归一化
//cout<< train_images <
cv::Ptr < cv::ml::SVM > svm = cv::ml::SVM::create();//创建svm
//设置类型为C_SVC代表分类
svm->setType(cv::ml::SVM::C_SVC);
//设置核函数
svm->setKernel(cv::ml::SVM::POLY);
////设置其它属性
svm->setGamma(3.0);
svm->setDegree(3.0);
////设置迭代终止条件
svm->setTermCriteria(cv::TermCriteria(cv::TermCriteria::MAX_ITER | cv::TermCriteria::EPS, 100, 0.0001));
//开始训练
cv::Ptr train_data = cv::ml::TrainData::create(train_images, cv::ml::ROW_SAMPLE, train_labels);
cout << "开始进行训练..." << endl;
svm->train(train_data);
cout << "训练完成" << endl;
Mat pixel = Mat_<float>(1, bands);//初始化一个长为波段数的mat，用于存放每一个像元
char* fenlei = (char*)malloc(sizeof(char) * src_width * src_height);//接收分类结果
for (int i = 0; i < src_height * src_width; i++)//循环遍历每一个像元
{
for (int j = 0; j < bands; j++)
{
pixel.at<float>(j) = data.at<float>(j, i);
}//把每一个像元的的值赋值给pixel，从而获得当前像元
//cout << pixel << endl;
fenlei[i] = svm->predict(pixel);
}
unsigned char** class_rt = new unsigned char*[3];
for (int i = 0; i < 3; i++)
{
class_rt[i] = new unsigned char[src_height * src_width];
}
for (int i = 0; i < src_height * src_width; i++)
{
if (fenlei[i] == 0)
{
class_rt[0][i] = 0;
class_rt[1][i] = 0;
class_rt[2][i] = 255;
}
else if (fenlei[i] == 1)
{
class_rt[0][i] = 255;
class_rt[1][i] = 0;
class_rt[2][i] = 255;
}
}
GDALDriver* podriver = (GDALDriver*)GDALGetDriverByName("GTiff");
GDALDataset* p_out = podriver->Create(outfile, src_width, src_height, 3, GDT_Byte, NULL);
for (int i = 0; i < 3; i++)
{
p_out->GetRasterBand(i + 1)->RasterIO(GF_Write, 0, 0, src_width, src_height, class_rt[i], src_width, src_height, GDT_Byte, 0, 0);
}
GDALClose(p_out);
}
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