HOG特征显示C++ OpenCV代码

参考：http://download.csdn.net/detail/u011285477/9472067

#include <opencv2/opencv.hpp>  #include <cstdio>  #include <cstdlib>  #include <Windows.h>  #include "opencvtest.h"using namespace std;using namespace cv;// HOGDescriptor visual_imagealizer// adapted for arbitrary size of feature sets and training imagesMat get_hogdescriptor_visual_image(Mat& origImg,vector<float>& descriptorValues,//hog特征向量Size winSize,//图片窗口大小Size cellSize,int scaleFactor,//缩放背景图像的比例double viz_factor)//缩放hog特征的线长比例{Mat visual_image;//最后可视化的图像大小resize(origImg, visual_image, Size(origImg.cols*scaleFactor, origImg.rows*scaleFactor));int gradientBinSize = 9;// dividing 180° into 9 bins, how large (in rad) is one bin?float radRangeForOneBin = 3.14 / (float)gradientBinSize; //pi=3.14对应180°// prepare data structure: 9 orientation / gradient strenghts for each cellint cells_in_x_dir = winSize.width / cellSize.width;//x方向上的cell个数int cells_in_y_dir = winSize.height / cellSize.height;//y方向上的cell个数int totalnrofcells = cells_in_x_dir * cells_in_y_dir;//cell的总个数//注意此处三维数组的定义格式//int ***b;//int a[2][3][4];//int (*b)[3][4] = a;//gradientStrengths[cells_in_y_dir][cells_in_x_dir][9]float*** gradientStrengths = new float**[cells_in_y_dir];int** cellUpdateCounter = new int*[cells_in_y_dir];for (int y = 0; y<cells_in_y_dir; y++){gradientStrengths[y] = new float*[cells_in_x_dir];cellUpdateCounter[y] = new int[cells_in_x_dir];for (int x = 0; x<cells_in_x_dir; x++){gradientStrengths[y][x] = new float[gradientBinSize];cellUpdateCounter[y][x] = 0;for (int bin = 0; bin<gradientBinSize; bin++)gradientStrengths[y][x][bin] = 0.0;//把每个cell的9个bin对应的梯度强度都初始化为0}}// nr of blocks = nr of cells - 1// since there is a new block on each cell (overlapping blocks!) but the last one//相当于blockstride = (8,8)int blocks_in_x_dir = cells_in_x_dir - 1;int blocks_in_y_dir = cells_in_y_dir - 1;// compute gradient strengths per cellint descriptorDataIdx = 0;int cellx = 0;int celly = 0;for (int blockx = 0; blockx<blocks_in_x_dir; blockx++){for (int blocky = 0; blocky<blocks_in_y_dir; blocky++){// 4 cells per block ...for (int cellNr = 0; cellNr<4; cellNr++){// compute corresponding cell nrint cellx = blockx;int celly = blocky;if (cellNr == 1) celly++;if (cellNr == 2) cellx++;if (cellNr == 3){cellx++;celly++;}for (int bin = 0; bin<gradientBinSize; bin++){float gradientStrength = descriptorValues[descriptorDataIdx];descriptorDataIdx++;gradientStrengths[celly][cellx][bin] += gradientStrength;//因为C是按行存储} // for (all bins)// note: overlapping blocks lead to multiple updates of this sum!// we therefore keep track how often a cell was updated,// to compute average gradient strengthscellUpdateCounter[celly][cellx]++;//由于block之间有重叠，所以要记录哪些cell被多次计算了} // for (all cells)} // for (all block x pos)} // for (all block y pos)// compute average gradient strengthsfor (int celly = 0; celly<cells_in_y_dir; celly++){for (int cellx = 0; cellx<cells_in_x_dir; cellx++){float NrUpdatesForThisCell = (float)cellUpdateCounter[celly][cellx];// compute average gradient strenghts for each gradient bin directionfor (int bin = 0; bin<gradientBinSize; bin++){gradientStrengths[celly][cellx][bin] /= NrUpdatesForThisCell;}}}cout << "winSize = " << winSize << endl;cout << "cellSize = " << cellSize << endl;cout << "blockSize = " << cellSize * 2 << endl;cout << "blockNum = " << blocks_in_x_dir << "×" << blocks_in_y_dir << endl;cout << "descriptorDataIdx = " << descriptorDataIdx << endl;// draw cellsfor (int celly = 0; celly<cells_in_y_dir; celly++){for (int cellx = 0; cellx<cells_in_x_dir; cellx++){int drawX = cellx * cellSize.width;int drawY = celly * cellSize.height;int mx = drawX + cellSize.width / 2;int my = drawY + cellSize.height / 2;rectangle(visual_image,Point(drawX*scaleFactor, drawY*scaleFactor),Point((drawX + cellSize.width)*scaleFactor,(drawY + cellSize.height)*scaleFactor),CV_RGB(0, 0, 0),//cell框线的颜色1);// draw in each cell all 9 gradient strengthsfor (int bin = 0; bin<gradientBinSize; bin++){float currentGradStrength = gradientStrengths[celly][cellx][bin];// no line to draw?if (currentGradStrength == 0)continue;float currRad = bin * radRangeForOneBin + radRangeForOneBin / 2;//取每个bin里的中间值，如10°,30°,...,170°.float dirVecX = cos(currRad);float dirVecY = sin(currRad);float maxVecLen = cellSize.width / 2;float scale = viz_factor; // just a visual_imagealization scale,// to see the lines better// compute line coordinatesfloat x1 = mx - dirVecX * currentGradStrength * maxVecLen * scale;float y1 = my - dirVecY * currentGradStrength * maxVecLen * scale;float x2 = mx + dirVecX * currentGradStrength * maxVecLen * scale;float y2 = my + dirVecY * currentGradStrength * maxVecLen * scale;// draw gradient visual_imagealizationline(visual_image,Point(x1*scaleFactor, y1*scaleFactor),Point(x2*scaleFactor, y2*scaleFactor),CV_RGB(255, 255, 255),//HOG可视化的cell的颜色1);} // for (all bins)} // for (cellx)} // for (celly)// don't forget to free memory allocated by helper data structures!for (int y = 0; y<cells_in_y_dir; y++){for (int x = 0; x<cells_in_x_dir; x++){delete[] gradientStrengths[y][x];}delete[] gradientStrengths[y];delete[] cellUpdateCounter[y];}delete[] gradientStrengths;delete[] cellUpdateCounter;return visual_image;//返回最终的HOG可视化图像}int main(){HOGDescriptor hog;//使用的是默认的hog参数/*HOGDescriptor(Size win_size=Size(64, 128), Size block_size=Size(16, 16), Size block_stride=Size(8, 8),Size cell_size=Size(8, 8), int nbins=9, double win_sigma=DEFAULT_WIN_SIGMA(DEFAULT_WIN_SIGMA=-1),double threshold_L2hys=0.2, bool gamma_correction=true, int nlevels=DEFAULT_NLEVELS)Parameters:win_size – Detection window size. Align to block size and block stride.block_size – Block size in pixels. Align to cell size. Only (16,16) is supported for now.block_stride – Block stride. It must be a multiple of cell size.cell_size – Cell size. Only (8, 8) is supported for now.nbins – Number of bins. Only 9 bins per cell are supported for now.win_sigma – Gaussian smoothing window parameter.threshold_L2hys – L2-Hys normalization method shrinkage.gamma_correction – Flag to specify whether the gamma correction preprocessing is required or not.nlevels – Maximum number of detection window increases.*///对于128*80的图片，blockstride = 8,15*9的block，2*2*9*15*9 = 4860Mat src = imread("E:\\C++WorkPlace\\myHOGvisualize\\myHOGvisualize\\img.jpg");//注意这里边的双斜杠！！！！！！！！！！int src_width = src.cols;int src_height = src.rows;int width = src_width;int height = src_height;hog.winSize = Size(width, height);vector<float> des;//HOG特征向量Mat dst;resize(src, dst, Size(width, height));//规范图像尺寸imshow("src", src);hog.compute(dst,des);//计算hog特征Mat background = Mat::zeros(Size(width,height),CV_8UC1);//设置黑色背景图，因为要用白色绘制hog特征Mat d = get_hogdescriptor_visual_image(background,des,hog.winSize,hog.cellSize,3,2.5);imshow("dst",d);imwrite("hogvisualize.jpg",d);waitKey();return 0;}