BOW-SURF影像检测.docx

BOW-SURF 天安门影像检测#include stdafx.h#include highgui.h#include opencv2/objdetect/objdetect.hpp#include opencv2/highgui/highgui.hpp#include opencv2/imgproc/imgproc.hpp#include opencv2/legacy/legacy.hpp#include opencv2/nonfree/features2d.hpp#include opencv2/ml/ml.hpp#include opencv2/core/core.hpp#include opencv2/contrib/contrib.hpp#include #include features2d (2).hpp#include opencv.hpp#include features2d.hpp#include #include using namespace std;using namespace cv;/*SURF训练，SURF提取*/void main(int argc, char *argv)DWORD start_time = GetTickCount();/结果输出 CString data;CString OutData;CString OutData_result;CString ResultPath = _T(E:/temp/result/BOW_SURF_result.txt);CFile file(ResultPath, CFile:modeCreate | CFile:modeWrite);file.SeekToBegin();file.Write(xffxfe, 2);Mat srcImage1 = imread(E:temp天安门.bmp);Mat *srcImage2 = new Mat100;int minHessian = 1000;SURF detector(minHessian);std:vector* train_keyPoint2 = new std:vector21;SURF extractor;Mat *train_descriptor2 = new Mat21;char imagepath100;char matchResult100;int tiananmen_tiananmen_count = 0;int tiananmen_qita_count = 0;int qita_tiananmen_count = 0;int qita_qita_count = 0;/训练得到BOW特征训练集detector.detect(srcImage1, train_keyPoint20);pute(srcImage1, train_keyPoint20, train_descriptor20);for (int s = 1; s 21; s+)sprintf_s(imagepath, E:temp天安门 (%d).jpg, s);srcImage2s = imread(imagepath);if (!srcImage1.data | !srcImage2s.data)printf(读取检测图片%d错误, s);/训练影像特征检测detector.detect(srcImage2s, train_keyPoint2s);pute(srcImage2s, train_keyPoint2s, train_descriptor2s);for (int s = 21; s 100; s+)sprintf_s(imagepath, E:temp其它 (%d).jpg, s - 20);srcImage2s = imread(imagepath);if (!srcImage1.data | !srcImage2s.data)printf(读取检测图片%d错误, s);BOWKMeansTrainer bowTraining(100); /定义聚类中心1000个，其余的默认参数；for (int i = 0; i 21; i+)bowTraining.add(train_descriptor2i);Mat dictionary = bowTraining.cluster();/ Obtain the BoF descriptor/FlannBasedMatcher *matcher = new std:vector;Ptr BF_matcher(new BFMatcher);/ brute force matcherPtr Flann_matcher(new FlannBasedMatcher);/ nearest neighbor matcherPtr SURF_detector(new SurfFeatureDetector);/ SURF detctorPtr SURF_extractor(new SurfDescriptorExtractor);/ SURF extractorBOWImgDescriptorExtractor SURF_bowDE(SURF_extractor, BF_matcher);/ BoF (or BoW) SURF descriptor extractorSURF_bowDE.setVocabulary(dictionary);/ Set the dictionary with the vocabulary/关键影像探测vector SURF_keyPoint1;/ keypoints extracted by SURFMat SURF_keyDescriptor1;/ BoF descriptor of key imageSURF_detector-detect(srcImage1, SURF_keyPoint1);/ feature points/ 使用缺省阈值SURF_bowDE.compute(srcImage1, SURF_keyPoint1, SURF_keyDescriptor1);/ descriptors for keypoints /检测影像探测std:vector* SURF_keyPoint2 = new std:vector100;/ Bof descriptor of current imageMat *SURF_descriptor2 = new Mat100;double *cosa=new double100;data.Format(_T(%s%drn), _T(COSA=0.809,minHessian=), minHessian);OutData += data;data.Format(_T(%srn%srn), _T(SURF训练，SURF提取，余弦距离筛选), _T(检测影像 检测结果);OutData += data;data.Format(_T(%s), _T(检测为天安门的影像有：);OutData_result += data;for (int i = 1; i detect(srcImage2i, SURF_keyPoint2i);SURF_bowDE.compute(srcImage2i, SURF_keyPoint2i, SURF_descriptor2i);cosai = SURF_keyDescriptor1.dot(SURF_descriptor2i)/ sqrt(SURF_keyDescriptor1.dot(SURF_keyDescriptor1) * SURF_descriptor2i.dot(SURF_descriptor2i);if (i 0.809)tiananmen_tiananmen_count+;printf(天安门（%d).jpg是rn, i);data.Format(_T(%s%d%s), _T(天安门(), i, _T().jpg、);OutData_result += data;elsetiananmen_qita_count+;printf(天安门（%d).jpg不是rn, i);elseif (cosai 0.809)qita_tiananmen_count+;printf(其它（%d).jpg是rn, i - 20);data.Format(_T(%s%d%s), _T(其它(), i - 20, _T().jpg、);OutData_result += data;elseqita_qita_count+;printf(其它（%d).jpg不是rn, i - 20);double cosa_sort100;Mat *src_sort = new Mat100;char imagepath_sort100100;for (int s = 1; s 100; s+)cosa_sorts = cosas;sort(&cosa_sort0, &cosa_sort100);for (int s1 = 1; s1 100; s1+)for (int s2 = 1; s2 100; s2+)if (cosas1 = cosa_sorts2)src_sorts2 = srcImage2s1;if (s1 21)sprintf_s(imagepath_sorts2, E:temp天安门 (%d).jpg, s1);elsesprintf_s(imagepath_sorts2, E:temp其它 (%d).jpg, s1 - 20);data.Format(_T(%srn), _T(排序后的结果,用于人工选取阈值);OutData += data;for (int s = 1; s 100; s+)printf(%s %.4frn, imagepath_sorts, cosa_sorts);data.Format(_T(%s %.4frn), CString(imagepath_sorts), cosa_sorts);OutData += data;printf(天安门检测为天安门的个数为%drn, tiananmen_tiananmen_count);printf(天安门检测为其它的个数为%drn, tiananmen_qita_count);printf(其它检测为天安门的个数为%drn, qita_tiananmen_count);printf(其它检测为其它的个数为%drn, qita_qita_count);OutData += OutData_result;float total_true_rate = float(tiananmen_tiananmen_count) / float(tiananmen_qita_count + qita_tiananmen_count);float true_rate = float(tiananmen_tiananmen_count) / float(tiananmen_qita_count + tiananmen_tiananmen_count);printf(总体检测正确率%frn, total_true_rate);printf(检测正确率%frn, true_rate);data.Format(_T(rn%s%.6frn), _T(总体检测正确率:), total_true_rate);OutData += data;data.Format(_T(%s%.6frn), _T(检测正确率:), true_rate);OutData += data;DWORD end_time = GetTickC