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opencv中使用K-近邻分类算法KNN
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K-近鄰（K-Nearest Neighbors, KNN）是一種很好理解的分類算法，簡單說來就是從訓練樣本中找出K個與其最相近的樣本，然后看這K個樣本中哪個類別的樣本多，則待判定的值（或說抽樣）就屬于這個類別。
KNN算法的步驟
- 計算已知類別數據集中每個點與當前點的距離；
- 選取與當前點距離最小的K個點；
- 統計前K個點中每個類別的樣本出現的頻率；
- 返回前K個點出現頻率最高的類別作為當前點的預測分類。
OpenCV中使用CvKNearest
OpenCV中實現CvKNearest類可以實現簡單的KNN訓練和預測。
[cpp]?view plaincopy
int?main()?? {?? ????float?labels[10]?=?{0,0,0,0,0,1,1,1,1,1};?? ????Mat?labelsMat(10,?1,?CV_32FC1,?labels);?? ????cout<<labelsMat<<endl;?? ????float?trainingData[10][2];?? ????srand(time(0));??? ????for(int?i=0;i<5;i++){?? ????????trainingData[i][0]?=?rand()%255+1;?? ????????trainingData[i][1]?=?rand()%255+1;?? ????????trainingData[i+5][0]?=?rand()%255+255;?? ????????trainingData[i+5][1]?=?rand()%255+255;?? ????}?? ????Mat?trainingDataMat(10,?2,?CV_32FC1,?trainingData);?? ????cout<<trainingDataMat<<endl;?? ????CvKNearest?knn;?? ????knn.train(trainingDataMat,labelsMat,Mat(),?false,?2?);?? ?????? ????int?width?=?512,?height?=?512;?? ????Mat?image?=?Mat::zeros(height,?width,?CV_8UC3);?? ????Vec3b?green(0,255,0),?blue?(255,0,0);?? ?? ????for?(int?i?=?0;?i?<?image.rows;?++i){?? ????????for?(int?j?=?0;?j?<?image.cols;?++j){?? ????????????const?Mat?sampleMat?=?(Mat_<float>(1,2)?<<?i,j);?? ????????????Mat?response;?? ????????????float?result?=?knn.find_nearest(sampleMat,1);?? ????????????if?(result?!=0){?? ????????????????image.at<Vec3b>(j,?i)??=?green;?? ????????????}?? ????????????else???? ????????????????image.at<Vec3b>(j,?i)??=?blue;?? ????????}?? ????}?? ?? ?????????? ????????for(int?i=0;i<5;i++){?? ????????????circle(?image,?Point(trainingData[i][0],??trainingData[i][1]),??? ????????????????5,?Scalar(??0,???0,???0),?-1,?8);?? ????????????circle(?image,?Point(trainingData[i+5][0],??trainingData[i+5][1]),??? ????????????????5,?Scalar(255,?255,?255),?-1,?8);?? ????????}?? ????????imshow("KNN?Simple?Example",?image);??? ????????waitKey(10000);?? ?? }?? 使用的是之前BP神經網絡中的例子，分類結果如下：
預測函數find_nearest()除了輸入sample參數外還有些其他的參數：
[cpp]?view plaincopy
float?CvKNearest::find_nearest(const?Mat&?samples,?int?k,?Mat*?results=0,??? const?float**?neighbors=0,?Mat*?neighborResponses=0,?Mat*?dist=0?)??
即，samples為樣本數*特征數的浮點矩陣；K為尋找最近點的個數；results與預測結果；neibhbors為k*樣本數的指針數組（輸入為const，實在不知為何如此設計）；neighborResponse為樣本數*k的每個樣本K個近鄰的輸出值；dist為樣本數*k的每個樣本K個近鄰的距離。
另一個例子
OpenCV refman也提供了一個類似的示例，使用CvMat格式的輸入參數：
[cpp]?view plaincopy
int?main(?int?argc,?char**?argv?)?? {?? ????const?int?K?=?10;?? ????int?i,?j,?k,?accuracy;?? ????float?response;?? ????int?train_sample_count?=?100;?? ????CvRNG?rng_state?=?cvRNG(-1);?? ????CvMat*?trainData?=?cvCreateMat(?train_sample_count,?2,?CV_32FC1?);?? ????CvMat*?trainClasses?=?cvCreateMat(?train_sample_count,?1,?CV_32FC1?);?? ????IplImage*?img?=?cvCreateImage(?cvSize(?500,?500?),?8,?3?);?? ????float?_sample[2];?? ????CvMat?sample?=?cvMat(?1,?2,?CV_32FC1,?_sample?);?? ????cvZero(?img?);?? ????CvMat?trainData1,?trainData2,?trainClasses1,?trainClasses2;?? ?????? ????cvGetRows(?trainData,?&trainData1,?0,?train_sample_count/2?);?? ????cvRandArr(?&rng_state,?&trainData1,?CV_RAND_NORMAL,?cvScalar(200,200),?cvScalar(50,50)?);?? ????cvGetRows(?trainData,?&trainData2,?train_sample_count/2,?train_sample_count?);?? ????cvRandArr(?&rng_state,?&trainData2,?CV_RAND_NORMAL,?cvScalar(300,300),?cvScalar(50,50)?);?? ????cvGetRows(?trainClasses,?&trainClasses1,?0,?train_sample_count/2?);?? ????cvSet(?&trainClasses1,?cvScalar(1)?);?? ????cvGetRows(?trainClasses,?&trainClasses2,?train_sample_count/2,?train_sample_count?);?? ????cvSet(?&trainClasses2,?cvScalar(2)?);?? ?????? ????CvKNearest?knn(?trainData,?trainClasses,?0,?false,?K?);?? ????CvMat*?nearests?=?cvCreateMat(?1,?K,?CV_32FC1);?? ????for(?i?=?0;?i?<?img->height;?i++?)?? ????{?? ????????for(?j?=?0;?j?<?img->width;?j++?)?? ????????{?? ????????????sample.data.fl[0]?=?(float)j;?? ????????????sample.data.fl[1]?=?(float)i;?? ?????????????? ????????????response?=?knn.find_nearest(&sample,K,0,0,nearests,0);?? ?????????????? ????????????for(?k?=?0,?accuracy?=?0;?k?<?K;?k++?)?? ????????????{?? ????????????????if(?nearests->data.fl[k]?==?response)?? ????????????????????accuracy++;?? ????????????}?? ?????????????? ????????????cvSet2D(?img,?i,?j,?response?==?1???? ????????????????(accuracy?>?5???CV_RGB(180,0,0)?:?CV_RGB(180,120,0))?:?? ????????????????(accuracy?>?5???CV_RGB(0,180,0)?:?CV_RGB(120,120,0))?);?? ????????}?? ????}?? ?????? ????for(?i?=?0;?i?<?train_sample_count/2;?i++?)?? ????{?? ????????CvPoint?pt;?? ????????pt.x?=?cvRound(trainData1.data.fl[i*2]);?? ????????pt.y?=?cvRound(trainData1.data.fl[i*2+1]);?? ????????cvCircle(?img,?pt,?2,?CV_RGB(255,0,0),?CV_FILLED?);?? ????????pt.x?=?cvRound(trainData2.data.fl[i*2]);?? ????????pt.y?=?cvRound(trainData2.data.fl[i*2+1]);?? ????????cvCircle(?img,?pt,?2,?CV_RGB(0,255,0),?CV_FILLED?);?? ????}?? ????cvNamedWindow(?"classifier?result",?1?);?? ????cvShowImage(?"classifier?result",?img?);?? ????cvWaitKey(0);?? ????cvReleaseMat(?&trainClasses?);?? ????cvReleaseMat(?&trainData?);?? ????return?0;?? }?? 分類結果：
KNN的思想很好理解，也非常容易實現，同時分類結果較高，對異常值不敏感。但計算復雜度較高，不適于大數據的分類問題。
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