17张图片中，有部分图片较模糊，行人影像较小，可能导致难以分辨。下面给出代码，，希望各位能够指正错误。说明：本代码全部由自己编写，所用函数未调用OpenCV实用库函数，及机器学习库函数（基本数据除外）。/***********************************************************//** Copyright by Weidi Xu, S.C.U.T in Guangzhou, Guangdong**//***********************************************************/#include <opencv2\opencv.hpp>#include <cstring>#include <cstdio>#include <cmath>#include <ctime>using std::clock_t;using std::clock;using namespace cv;//const parameters for imageconst int NUM_NEGIMAGE = 1000;const int NUM_POSIMAGE = 500;const int NUM_IMAGE = 1500;const int NUM_TESTIMAGE = 22;const int MAX_DIMENSION = 3781;const int IMAGE_ROWS = 128;const int IMAGE_COLS = 64;const int CELLSIZE = 8;const int BLOCKSIZE = 16;const int MOVELENGTH = 8;const int BINSIZE = 9;const double PI = 2*acos(0.0);const double eps = 1e-8;//mediate parameterconst int NUM_BLOCK_ROWS = (IMAGE_ROWS-BLOCKSIZE)/MOVELENGTH+1;const int NUM_BLOCK_COLS = (IMAGE_COLS-BLOCKSIZE)/MOVELENGTH+1;const int NUM_BLOCK_FEATURES = (BLOCKSIZE/CELLSIZE)*(BLOCKSIZE/CELLSIZE)*BINSIZE+1;//zero for theta[0]//data from image//since the features in the adaboost should contain a single block, it’s better to define the feature of 3-dimension;double features[NUM_IMAGE][NUM_BLOCK_ROWS][NUM_BLOCK_COLS][NUM_BLOCK_FEATURES];double type[NUM_IMAGE]; //1 – pos, 0 – negdouble y[NUM_IMAGE]; //1 – pos, -1 – neg//number of weak classifier(changing in experiment)const int NUM_WEAKCLASSIFIER = 100;//data for adaboostdouble weight[NUM_IMAGE];//logistic function(dimension is given by NUM_BLOCK_FEATURES(37 in this setting))double logistic(double theta[], double x[]){double ans = 0;for(int i = 0 ; i < NUM_BLOCK_FEATURES; i++){ans += theta[i]*x[i];}return 1/(1+std::exp(-ans));}struct WeakClassifier{double _theta[NUM_BLOCK_FEATURES]; //threshold classifierint _index_row; //classify by the features in block[_block_row][_block_y]int _index_col;int _isreverse; //1 for (> := pos, < := neg); 0 for (< := pos, >:= neg)double _alpha; double _error;void clear(){memset(_theta, 0.0, NUM_BLOCK_FEATURES*sizeof(double));_alpha = 0.0;_error = 1;_index_row = -1;_index_col = -1;_isreverse = true;}//return 1 or -1int cal(double x[NUM_BLOCK_ROWS][NUM_BLOCK_COLS][NUM_BLOCK_FEATURES]){int ans = logistic(_theta, x[_index_row][_index_col]);if(ans > 0.5){if(_isreverse)return -1;elsereturn 1;}else{if(_isreverse)return 1;elsereturn -1;}}void print(){//thetafor(int i = 0 ; i < NUM_BLOCK_FEATURES; i++)printf("%lf ", _theta[i]);printf("\n");//int _index_row;printf("%d ",_index_row);//int _index_col;printf("%d ",_index_col);//int _isreverse; //1 for (> := pos, < := neg); 0 for (< := pos, >:= neg)printf("%d ",_isreverse);//double _alpha;printf("%lf ",_alpha);//double _error;printf("%lf \n",_error);}}weakClassifier[NUM_WEAKCLASSIFIER];//Util Functiondouble arc2angle(double arc){return arc/PI*180.0;}double angle2arc(double angle){return angle/180.0*PI;}void posfilename(int i, char* filename){sprintf(filename, "pos/pos (%d).png", i);return;}void negfilename(int i, char* filename){sprintf(filename, "neg/neg (%d).png", i);return;}void testfilename(int i, char* filename){sprintf(filename, "test_pos/test (%d).png", i);return ;}//I(x,y) = sqrt(I(x,y))void normalizeImage(Mat& inputImage){// accept only char type matricesCV_Assert(inputImage.depth() != sizeof(uchar));int channels = inputImage.channels();int nRows = inputImage.rows ;int nCols = inputImage.cols* channels;if (inputImage.isContinuous()){nCols *= nRows;nRows = 1;}int i,j;uchar* p;for( i = 0; i < nRows; ++i){p = inputImage.ptr<uchar>(i);for ( j = 0; j < nCols; ++j){p[j] = int(sqrt(p[j]*1.0));}}return;}//I(x,y) 第一维的梯度为xGradientvoid calGredient(const Mat& inputImage, double xGradient[IMAGE_ROWS][IMAGE_COLS], double yGradient[IMAGE_ROWS][IMAGE_COLS]){uchar* dataptr = inputImage.data;int nrows = inputImage.rows;int ncols = inputImage.cols;//cal xgradientfor(int i = 1 ; i < nrows – 1; i++){for(int j = 0 ; j < ncols; j++){xGradient[i][j] = inputImage.at<uchar>(i+1,j) – inputImage.at<uchar>(i-1,j);}}//cal marginfor(int i = 0 ; i < ncols; i++){xGradient[0][i] = (inputImage.at<uchar>(1,i) – inputImage.at<uchar>(0,i))*2;xGradient[nrows-1][i] = (inputImage.at<uchar>(nrows-1,i) – inputImage.at<uchar>(nrows-2,i))*2;}//cal ygradientfor(int i = 0 ; i < nrows ; i++){for(int j = 1 ; j < ncols – 1; j++){yGradient[i][j] = inputImage.at<uchar>(i,j+1) – inputImage.at<uchar>(i,j-1);}}//cal marginfor(int i = 0 ; i < nrows; i++){xGradient[i][0] = (inputImage.at<uchar>(i,1) – inputImage.at<uchar>(i,0))*2;xGradient[i][ncols-1] = (inputImage.at<uchar>(i,ncols-1) – inputImage.at<uchar>(i,ncols-2))*2;}}//cal the HogFeatures by blockvoid calHogFeatures(Mat& inputImage, double outputFeature[NUM_BLOCK_ROWS][NUM_BLOCK_COLS][NUM_BLOCK_FEATURES]){int nrows = inputImage.rows;int ncols = inputImage.cols;int type = inputImage.type();if(nrows != IMAGE_ROWS || ncols != IMAGE_COLS)abort();//cal x,yGradientdouble xGradient[IMAGE_ROWS][IMAGE_COLS];double yGradient[IMAGE_ROWS][IMAGE_COLS];calGredient(inputImage, xGradient, yGradient);//computation mediandouble gradient[IMAGE_ROWS][IMAGE_COLS];double direction[IMAGE_ROWS][IMAGE_COLS];for(int i = 0 ; i < nrows; i++){for(int j = 0 ; j < ncols; j++){double gx = xGradient[i][j];double gy = yGradient[i][j];gradient[i][j] = sqrt(gx*gx + gy*gy);direction[i][j] = arc2angle(atan2(gy, gx));}}//compute cellinfo 8*8double cellinfo[IMAGE_ROWS/CELLSIZE][IMAGE_COLS/CELLSIZE][BINSIZE];memset(cellinfo, 0, sizeof(cellinfo));for(int i = 0; i < IMAGE_ROWS/CELLSIZE; i++){for(int j = 0 ; j < IMAGE_COLS/CELLSIZE; j++){double* cell = cellinfo[i][j];//cal single cellinfo of 8*8for(int ci = 0 ; ci < CELLSIZE; ci++){for(int cj = 0; cj < CELLSIZE; cj++){//find org pix;int px = i*CELLSIZE + ci;int py = j*CELLSIZE + cj;int binindex = int((direction[px][py]+180.0)/(360.0/BINSIZE));//handle boundif(fabs(direction[px][py]-180) < eps){binindex = BINSIZE-1;}if(fabs(direction[px][py]+180) < eps){binindex = 0;}if(binindex < 0 || binindex >= BINSIZE){printf("Wrong binindex: %d %lf %lf %lf", binindex, xGradient[px][py], yGradient[px][py], direction[px][py]);abort();}cell[binindex] += gradient[px][py];}}}}/*double blockinfo[(IMAGE_ROWS-BLOCKSIZE)/MOVELENGTH+1][(IMAGE_COLS-BLOCKSIZE)/MOVELENGTH+1][(BLOCKSIZE/CELLSIZE)*(BLOCKSIZE/CELLSIZE)*BINSIZE];*/if(MOVELENGTH%CELLSIZE != 0){printf("MOVELENGTH%CELLSIZE != 0");abort();}//cal blockinfofor(int i = 0 ; i < (IMAGE_ROWS-BLOCKSIZE)/MOVELENGTH + 1; i++){for(int j = 0 ; j < (IMAGE_COLS-BLOCKSIZE)/MOVELENGTH + 1; j++){int bfindex = 0; outputFeature[i][j][bfindex++] = 1;//cal the position of this blockfor(int c1 = 0; c1 < BLOCKSIZE/CELLSIZE; c1++){for(int c2 = 0 ; c2 < BLOCKSIZE/CELLSIZE; c2++){//cal the index of cellint cx = i*MOVELENGTH/CELLSIZE+c1;int cy = j*MOVELENGTH/CELLSIZE+c2;for(int binindex = 0 ; binindex < BINSIZE; binindex++){outputFeature[i][j][bfindex++] = cellinfo[cx][cy][binindex];}}}}}return;}//use global variablesvoid trainLogisticRegression(int block_row,int block_col, double theta[], double& errorrate, int& isreverse){double theta1[NUM_BLOCK_FEATURES], theta2[NUM_BLOCK_FEATURES];memset(theta1, 0, NUM_BLOCK_FEATURES*sizeof(double));memset(theta2, 0, NUM_BLOCK_FEATURES*sizeof(double));double errorrate1 = 0;double errorrate2 = 0;double rightnum1 = 0;double rightnum2 = 0;isreverse = 0;//cal parameter thetasfor(int k = 0 ; k < 100000; k++){int i = rand()%NUM_IMAGE;int j = rand()%NUM_BLOCK_FEATURES;theta1[j] = theta1[j] + weight[i]*0.01*(type[i] – logistic(theta1, features[i][block_row][block_col]))*features[i][block_row][block_col][j];}for(int i = 0 ; i < NUM_IMAGE; i++){double tmp = logistic(theta1, features[i][block_row][block_col]);if(tmp > 0.5 && fabs(type[i] – 1) < eps)rightnum1 += 1.0*weight[i];if(tmp < 0.5 && fabs(type[i] – 0) < eps)rightnum1 += 1.0*weight[i];}errorrate1 = 1 – rightnum1;//calreversefor(int k = 0 ; k < 100000; k++){int i = rand()%NUM_IMAGE;int j = rand()%NUM_BLOCK_FEATURES;theta2[j] = theta2[j] + weight[i]*0.01*(1- type[i] – logistic(theta2, features[i][block_row][block_col]))*features[i][block_row][block_col][j];}for(int i = 0 ; i < NUM_IMAGE; i++){double tmp = logistic(theta2, features[i][block_row][block_col]);if(tmp > 0.5 && fabs(type[i] – 0) < eps)rightnum2 += 1.0*weight[i];if(tmp < 0.5 && fabs(type[i] – 1) < eps)rightnum2 += 1.0*weight[i];}errorrate2 = 1 – rightnum2;if(errorrate1 < errorrate2){for(int i = 0 ; i < NUM_BLOCK_FEATURES; i++){theta[i] = theta1[i];}isreverse = 0;errorrate = errorrate1 + eps;}else{for(int i = 0 ; i < NUM_BLOCK_FEATURES; i++){theta[i] = theta2[i];}isreverse = 1;errorrate = errorrate2 + eps;}return;}WeakClassifier trainClassifier(){WeakClassifier ansclassifier;double theta[NUM_BLOCK_FEATURES];double errorrate = 1;int isreverse = 0;double best_theta[NUM_BLOCK_FEATURES];double best_errorrate = 1;int best_row = -1;int best_col = -1;int best_isreverse = 0;//select best weak classifierfor(int i = 0 ; i < NUM_BLOCK_ROWS; i++){for(int j = 0 ; j < NUM_BLOCK_COLS; j++){trainLogisticRegression(i,j,theta,errorrate, isreverse);if(errorrate < 0){printf("Wrong errorrate < 0 : %lf", errorrate);abort();}if(errorrate < best_errorrate){for(int tempi = 0 ; tempi < NUM_BLOCK_FEATURES; tempi++){best_theta[tempi] = theta[tempi];}best_errorrate = errorrate;best_row = i;best_col = j;best_isreverse = isreverse;}}}if(best_errorrate > 0.5){printf("The best_errorrate is greater than 0.5.\n");abort();}//set parameters;ansclassifier._alpha = 1.0/2*std::log((1-best_errorrate)/best_errorrate);ansclassifier._error = best_errorrate;ansclassifier._index_col = best_col;ansclassifier._index_row = best_row;ansclassifier._isreverse = best_isreverse;for(int i = 0 ; i < NUM_BLOCK_FEATURES; i++) ansclassifier._theta[i] = best_theta[i];return ansclassifier;}int calByStrongClassifier(double x[NUM_BLOCK_ROWS][NUM_BLOCK_COLS][NUM_BLOCK_FEATURES]){double ans = 0;for(int i = 0 ; i < NUM_WEAKCLASSIFIER; i++){ans += weakClassifier[i]._alpha * weakClassifier[i].cal(x);}if(ans > 0)return 1;elsereturn -1;}/*size: 128*64;type: CV_8UC1;Block大小为18*18；Cell大小为6*6；Block在检测窗口中上下移动尺寸为6*6；1个cell的梯度直方图化成9个bin；滑动窗口在检测图片中滑动的尺寸为6*6；*/int main(){char filename[100];IplImage* inputImage = NULL;clock_t timecount = clock();//load posimagefor(int i = 0 ; i < NUM_POSIMAGE; i++){posfilename(i+1 ,filename);//load grey image: set the parameter to 0;inputImage = cvLoadImage(filename, 0);//cal features;Mat inputMat(inputImage);calHogFeatures(inputMat, features[i]);type[i] = 1;y[i] = 1;//printf("%d \n", inputMat.cols);//release memoryinputMat.release();cvReleaseImage(&inputImage);inputImage = NULL;}printf("The feature process of pos-image have done in %d second.\n", (clock()-timecount)/1000);timecount = clock();//load neg imagesfor(int i = 0; i < NUM_NEGIMAGE; i++){negfilename(i+1, filename);//load grey image: set the parameter to 0;inputImage = cvLoadImage(filename, 0);type[NUM_POSIMAGE+i] = 0;y[NUM_POSIMAGE+i] = -1;Mat inputMat(inputImage);calHogFeatures(inputMat, features[NUM_POSIMAGE+i]);//release memoryinputMat.release();cvReleaseImage(&inputImage);inputImage = NULL;}printf("The feature process of neg-image have done in %d second.\n", (clock()-timecount)/1000);timecount = clock();//init weight arrayfor(int i = 0 ; i < NUM_IMAGE; i++){weight[i] = 1.0/NUM_IMAGE;}//freopenfreopen("HOG_CLASSIFIER.txt", "w", stdout);//print number of weakclassifiers;printf("%d\n", NUM_WEAKCLASSIFIER);//adaboost frameworkfor(int classifierindex = 0 ; classifierindex < NUM_WEAKCLASSIFIER; classifierindex++){weakClassifier[classifierindex] = trainClassifier();double error = weakClassifier[classifierindex]._error;double alpha = weakClassifier[classifierindex]._alpha;//printf("%d classifier: %lf ====\n",classifierindex, error);//printf("_index_row %d _index_col %d\n", weakClassifier[classifierindex]._index_row, weakClassifier[classifierindex]._index_col);double identitysum = 0;for(int sampleindex = 0 ; sampleindex < NUM_IMAGE; sampleindex++){weight[sampleindex] *= std::exp(-alpha*y[sampleindex]*weakClassifier[classifierindex].cal(features[sampleindex]));identitysum += weight[sampleindex];}//reweightfor(int sampleindex = 0 ; sampleindex < NUM_IMAGE; sampleindex++){weight[sampleindex] /= identitysum;}weakClassifier[classifierindex].print();}freopen("CON", "w", stdout);int rightnum = 0;for(int testindex = 0 ;testindex < NUM_TESTIMAGE; testindex ++){//posfilename(testindex+1, filename);testfilename(testindex+1, filename);inputImage = cvLoadImage(filename, 0);double testfeatures[NUM_BLOCK_ROWS][NUM_BLOCK_COLS][NUM_BLOCK_FEATURES];memset(testfeatures, 0, sizeof(testfeatures));Mat inputMat(inputImage);calHogFeatures(inputMat, testfeatures);if(calByStrongClassifier(testfeatures) == 1){rightnum++;//printf("Yes\n");}else//printf("No\n");inputMat.release();}printf("Accuracy: %d\n", rightnum);}//测试数据是网上流行的128*64灰度行人图像数据。
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