基本信息
源码名称:基于SVM与BoW的图片分类的OpenCV实现-svm图片分类
源码大小:0.04M
文件格式:.rar
开发语言:C/C++
更新时间:2015-12-14
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源码介绍
#include <iostream>
#include <fstream>
#include <string>
#include <map>
#include <iomanip>
#include <sstream>
#include "ImageClassPredictor.h"
#include "SVMPredictor.h"
#ifdef _DEBUG
#pragma comment(lib, "opencv_core245d")
#pragma comment(lib, "opencv_highgui245d")
#pragma comment(lib, "opencv_features2d245d")
#pragma comment(lib, "opencv_ml245d")
#pragma comment(lib, "opencv_nonfree245d")
#pragma comment(lib, "opencv_legacy245d")
#pragma comment(lib, "opencv_flann245d")
#pragma comment(lib, "opencv_imgproc245d")
#else
#pragma comment(lib, "opencv_core245")
#pragma comment(lib, "opencv_highgui245")
#pragma comment(lib, "opencv_features2d245")
#pragma comment(lib, "opencv_ml245")
#pragma comment(lib, "opencv_nonfree245")
#pragma comment(lib, "opencv_legacy245")
#pragma comment(lib, "opencv_flann245")
#pragma comment(lib, "opencv_imgproc245")
#endif
#define _LOG_RESULT_ 1
std::string image_folder_path("C:\\Users\\Hongze Zhao\\Downloads\\MLKD-Final-Project-Release\\ic-data\\extra\\");
bool ReadImageNamesAndLabels(std::vector<std::string>& image_file_names, std::vector<std::string>& image_labels, std::string list_file_name)
{
using namespace std;
ifstream label_list_file(image_folder_path list_file_name);
if (!label_list_file)
return false;
string fname;
string label;
while (!label_list_file.eof())
{
label_list_file >> fname >> label;
if (fname.length() == 0 || label.length() == 0)
continue;
image_file_names.push_back(fname);
image_labels.push_back(label);
}
label_list_file.close();
return true;
}
bool ReadImageNames(std::vector<std::string>& image_file_names, std::string list_file_name)
{
using namespace std;
ifstream label_list_file(image_folder_path list_file_name);
if (!label_list_file)
return false;
string fname;
string line;
while (!label_list_file.eof())
{
getline(label_list_file, line);
stringstream ss(line);
ss >> fname;
if (fname.length() == 0)
continue;
image_file_names.push_back(fname);
}
label_list_file.close();
return true;
}
int main(int argc, char* argv[])
{
using namespace cv;
using namespace std;
if (argc != 5)
{
std::cout << "Usage: " << argv[0] << " <vocabulary_file_name> <SVM_Classifier_file_prefix> <image folder> <image_name_list>" << endl;
std::cout << " eg. " << argv[0] << "vocabulary_color_surf_2000.yml SVM_classifier_color_blur_flann_ \"C:\\images\\\" test.list" << endl;
return -1;
}
image_folder_path = argv[3];
vector<string> image_file_names;
#if _LOG_RESULT_
ReadImageNames(image_file_names, argv[4]);
#else
vector<string> image_labels;
ReadImageNamesAndLabels(image_file_names, image_labels, argv[4]);
#endif
ImageClassPredictor predictor(argv[1], argv[2]);
//SVMPredictor predictor("vocabulary_color_blur_500.yml", "SVM_classifier_color_blur_brute_multi.yml");
std::cout << "predicting images ... " << std::endl;
map<std::string, int> correct_counts;
map<std::string, int> total_counts;
map<std::string, map<std::string, int>> confusion_matrix;
char buff_i[4];
char buff_j[4];
for (int i = 1; i <= 10; i )
{
itoa(i, buff_i, 10);
for (int j = 1; j <= 10; j )
{
itoa(j, buff_j, 10);
confusion_matrix[buff_i][buff_j] = 0;
}
}
#if _LOG_RESULT_
ofstream output("predict_results.txt", ios::out);
#endif
#pragma omp parallel for schedule(dynamic)
for (int i = 0; i < image_file_names.size(); i )
{
#if !(_LOG_RESULT_)
string& class_label = image_labels[i];
#endif
Mat image = imread(image_folder_path image_file_names[i] ".jpg");
if (image.rows == 0 || image.cols == 0)
{
cout << endl << image_file_names[i] << " read error!" << endl;
continue;
}
string predict_label = predictor.PredictClass(image);
#pragma omp critical
{
std::cout << "\b\b\b\b\b\b\b\b\b";
std::cout << setfill(' ') << setw(4) << i 1 << "/" << setw(4) << image_file_names.size();
#if _LOG_RESULT_
output << image_file_names[i] << "\t" << predict_label << endl;
#else
bool predict_correct = predict_label == class_label;
correct_counts[class_label] = (predict_correct ? 1 : 0);
total_counts[class_label] ;
confusion_matrix[class_label][predict_label] ;
std::cout << " correct? " << (predict_correct ? "yes" : "no") << " (" << predict_label << " - " << class_label << ")" << endl;
#endif
}
}
std::cout << endl;
#if _LOG_RESULT_
output.close();
#else
// print confusion matrix
for (map<std::string, map<std::string, int>>::iterator real_ite = confusion_matrix.begin(); real_ite != confusion_matrix.end(); real_ite)
{
float class_total = (float)total_counts[real_ite->first];
for (map<std::string, int>::iterator predict_ite = real_ite->second.begin(); predict_ite != real_ite->second.end(); predict_ite)
{
std::cout << setw(4) << predict_ite->second / class_total * 100.0 << " ";
}
std::cout << endl;
}
// print evaluate result
int total_correct = 0;
for (map<std::string, int>::iterator ite = correct_counts.begin(); ite != correct_counts.end(); ite)
{
int class_total = total_counts[ite->first];
float percent = (float)ite->second / (float)class_total * 100.0;
std::cout << "Class " << ite->first << ": " << ite->second << " / " << class_total << " (" << percent << " %)" << endl;
total_correct = ite->second;
}
float total_percent = (float)total_correct / (float)image_file_names.size() * 100.0;
std::cout << endl << "Total correct:" << total_correct << " / " << image_file_names.size() << " (" << total_percent << " %)" << endl;
#endif
::system("pause");
return 0;
}