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我正在尝试对异常和正常的视频帧进行聚类。我将帧分为正常帧和异常帧。我有两个问题，我不确定我的方法是否正确，并且出现了意外错误。请帮我。错误代码：bowTrainer.add(features1); 我的完整代码如下： // Bow.cpp : 定义控制台应用程序的入口点。//

#include "stdafx.h"
#include "opencv2/video/tracking.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/features2d/features2d.hpp"
#include <opencv2/nonfree/features2d.hpp>
#include <opencv2/nonfree/nonfree.hpp>
#include <opencv2/legacy/legacy.hpp>
#include <windows.h>
#include "opencv2/ml/ml.hpp"

#include <stdlib.h>
#include <stdio.h>

#include <sys/stat.h>
#define _USE_MATH_DEFINES
#include <math.h>

#include <limits>
#include <cstdio>
#include <iostream>
#include <fstream>

using namespace std;
using namespace cv;
using std::vector;
using std::iostream;

int main()
{
initModule_nonfree();

Ptr<FeatureDetector> features = FeatureDetector::create("SIFT");
Ptr<DescriptorExtractor> descriptor = DescriptorExtractor::create("SIFT");
Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("FlannBased");

//defining terms for bowkmeans trainer
TermCriteria tc(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 10, 0.001);
int dictionarySize = 100;
int retries = 1;
int flags = KMEANS_PP_CENTERS;
BOWKMeansTrainer bowTrainer(dictionarySize, tc, retries, flags);

BOWImgDescriptorExtractor bowDE(descriptor, matcher);

//**creating dictionary**//
Mat trainme(0, dictionarySize, CV_32FC1); 
Mat labels(0, 1, CV_32FC1); //1d matrix with 32fc1 is requirement of normalbayesclassifier class

int i=0;
while(i<10)
{
char filename[255]; 
string n;

n=sprintf(filename, "C:\\Users\\Desktop\\New folder\\View_001\\frame_000%d.jpg",i); 


Mat img = imread(filename, 0);
Mat features1;
vector<KeyPoint> keypoints;
descriptor->compute(img, keypoints, features1);

bowTrainer.add(features1);
Mat dictionary = bowTrainer.cluster();
bowDE.setVocabulary(dictionary);

Mat bowDescriptor;
bowDE.compute(img, keypoints, bowDescriptor);
trainme.push_back(bowDescriptor);
float label = 1.0;
labels.push_back(label);
      i++;
}


int  j=11;
while(j<21)
{
char filename2[255]; 
string n;

n=sprintf(filename2, "C:\\Users\\Desktop\\New folder\\View_001\\frame_000%d.jpg",j); 
      cout<<filename2;

Mat img2 = imread(filename2, 0);
Mat features2;
vector<KeyPoint> keypoints2;
descriptor->compute(img2, keypoints2, features2);
bowTrainer.add(features2);
Mat bowDescriptor2;
bowDE.compute(img2, keypoints2, bowDescriptor2);
trainme.push_back(bowDescriptor2);
float label = 2.0;
labels.push_back(label);
      j++;
}


NormalBayesClassifier classifier;
classifier.train(trainme, labels);

//**classifier trained**//

//**now trying to predict using the same trained classifier, it should return 1.0**//

Mat tryme(0, dictionarySize, CV_32FC1);
Mat tryDescriptor;
Mat img3 = imread("C:\\Users\\Desktop\\New folder\\View_001\\frame_0121.jpg", 0);
vector<KeyPoint> keypoints3;
features->detect(img3, keypoints3);
bowDE.compute(img3, keypoints3, tryDescriptor);
tryme.push_back(tryDescriptor);

cout<<classifier.predict(tryme)<<endl;
waitKey(0);
system("PAUSE");



return 0;
}
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