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| #include <opencv2/opencv.hpp>
#include <ctime>
#include <fstream>
#include <iostream>
#include <ostream>
#include <string>
#include <vector>
using namespace std;
using namespace cv;
using namespace cv::ml;
int reverseDigit(int num) {
unsigned char c1, c2, c3, c4;
c1 = num & 255;
c2 = (num >> 8) & 255;
c3 = (num >> 16) & 255;
c4 = (num >> 24) & 255;
return ((int)c1 << 24) + ((int)c2 << 16) + ((int)c3 << 8) + c4;
}
Mat readImagesData(int mode) {
ifstream f;
switch (mode) {
case 0:
f.open("train-images.idx3-ubyte", ios::binary);
cout << "读取训练图像数据" << endl;
break;
case 1:
f.open("t10k-images.idx3-ubyte", ios::binary);
cout << "读取测试图片数据" << endl;
break;
default:
break;
}
if (!f.is_open()) {
cout << "无法读取图像数据" << endl;
exit(-1);
}
int magic_number = 0;
int number_of_images = 0;
int height = 0;
int width = 0;
f.read((char *)&magic_number, sizeof(magic_number));
magic_number = reverseDigit(magic_number);
f.read((char *)&number_of_images, sizeof(number_of_images));
number_of_images = reverseDigit(number_of_images);
cout << "图像数量是：" << number_of_images;
f.read((char *)&height, sizeof(height));
height = reverseDigit(height);
f.read((char *)&width, sizeof(width));
width = reverseDigit(width);
Mat train_images = Mat(number_of_images, height * width, CV_8UC1);
for (int i = 0; i < number_of_images; i++) {
for (int r = 0; r < height; ++r) {
for (int c = 0; c < width; ++c) {
unsigned char temp = 0;
f.read((char *)&temp, sizeof(temp));
train_images.at<uchar>(i, r * width + c) = (int)temp;
if (i == 0) {
Mat digit = Mat(height, width, CV_8UC1);
digit.at<uchar>(r, c) = (int)temp;
imwrite("digit.png", digit);
}
}
}
}
train_images.convertTo(train_images, CV_32F);
f.close();
return train_images;
}
Mat readLabelsData(int mode) {
ifstream f;
switch (mode) {
case 0:
f.open("train-labels.idx1-ubyte");
cout << "读取训练标签" << endl;
break;
case 1:
f.open("t10k-labels.idx1-ubyte");
cout << "读取测试标签" << endl;
break;
default:
break;
}
if (!f.is_open()) {
cout << "无法读取标签数据" << endl;
exit(-1);
}
int magic_number = 0;
int number_of_labels = 0;
f.read((char *)&magic_number, sizeof(magic_number));
magic_number = reverseDigit(magic_number);
f.read((char *)&number_of_labels, sizeof(number_of_labels));
number_of_labels = reverseDigit(number_of_labels);
cout << "标签数量为：" << number_of_labels << endl;
Mat labels = Mat(number_of_labels, 1, CV_8UC1);
for (long int i = 0; i < number_of_labels; i++) {
unsigned char temp = 0;
f.read((char *)&temp, sizeof(temp));
labels.at<uchar>(i, 0) = temp;
}
labels.convertTo(labels, CV_32S);
f.close();
return labels;
}
int main() {
double time_start = (double)clock();
Mat train_images = readImagesData(0);
if (train_images.size == 0)
return -1;
Mat train_labels = readLabelsData(0);
if (train_labels.size == 0)
return -1;
cout << "成功读取图像和标签" << endl;
Ptr<ml::SVM> svm = ml::SVM::create();
svm->setType(ml::SVM::C_SVC);
svm->setKernel(ml::SVM::LINEAR);
svm->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER, 1000, FLT_EPSILON));
cout << "SVM训练开始。。。" << endl;
Ptr<ml::TrainData> tdata = ml::TrainData::create(train_images, ml::ROW_SAMPLE, train_labels);
svm->train(tdata);
svm->save("svm.xml");
double time_end = (double)clock();
cout << "SVM训练数据已保存" << endl;
cout << "SVM训练耗时：" << (time_end - time_start) / 1000.0 << "ms" << endl;
cout << "开始导入测试数据" << endl;
Mat tData = readImagesData(1);
if (tData.size == 0)
return -1;
Mat tLabel = readLabelsData(1);
if (tLabel.size == 0)
return -1;
cout << "已成功导入测试数据" << endl;
float count = 0;
for (int i = 0; i < tData.rows; i++) {
Mat sample = tData.row(i);
float res = svm->predict(sample);
res = std::abs(res - tLabel.at<unsigned int>(i, 0)) <= FLT_EPSILON ? 1.0f : 0.0f;
count += res;
}
double _rate = (count + 0.0) / 10000 * 100.0;
cout << "准确率为：" << _rate << "%..." << endl;
return 0;
}
|
