https://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 images
Mat 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;
}