HOG特征显示 可视化HOG

这是一个如何可视化hog特征的函数，转自：
http://blog.csdn.net/sinat_31135199/article/details/53470879

#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 cell    int 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 cell    int 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 nr                int 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 strengths                cellUpdateCounter[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 strengths    for (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 direction            for (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 cells    for (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 strengths            for (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 coordinates                float 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_imagealization                line(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 = 4860    Mat 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;}