Hog feature can computer easy using HOGDescriptor method in opencv.
Visualization is little bit complex. But JΓΌrgen Brauer introduce HOG feature visualization function in his blog.
refer to this page -> http://www.juergenwiki.de/work/wiki/doku.php?id=public:hog_descriptor_computation_and_visualization
I thought he is very excellent researcher in computer vision.
So, I introduce hog feature extraction and visualization using his code.
#include < opencv2\opencv.hpp>
#include < stdio.h>
#ifdef _DEBUG
#pragma comment(lib, "opencv_core249d.lib")
#pragma comment(lib, "opencv_imgproc249d.lib") //MAT processing
#pragma comment(lib, "opencv_highgui249d.lib")
#pragma comment(lib, "opencv_objdetect249d.lib")
#else
#pragma comment(lib, "opencv_core249.lib")
#pragma comment(lib, "opencv_imgproc249.lib")
#pragma comment(lib, "opencv_highgui249.lib")
#pragma comment(lib, "opencv_objdetect249.lib")
#endif
#define M_PI 3.1415
using namespace std;
using namespace cv;
Mat get_hogdescriptor_visual_image(Mat& origImg,
vector< float>& descriptorValues,
Size winSize,
Size cellSize,
int scaleFactor,
double viz_factor);
int main()
{
Mat img1 = imread("./1.jpg");
Mat img1_gray;
cvtColor(img1, img1_gray, CV_RGB2GRAY);
Mat r_img1_gray;
resize(img1_gray, r_img1_gray, Size(32, 16));
//extractino hog feature
HOGDescriptor d1( Size(32,16), Size(8,8), Size(4,4), Size(4,4), 9);
// Size(32,16), //winSize
// Size(8,8), //blocksize
// Size(4,4), //blockStride,
// Size(4,4), //cellSize,
// 9, //nbins,
//feature compare
vector< float> descriptorsValues1;
vector< Point> locations1;
d1.compute( r_img1_gray, descriptorsValues1, Size(0,0), Size(0,0), locations1);
//hog visualization
Mat r1 = get_hogdescriptor_visual_image(r_img1_gray,
descriptorsValues1,
Size(32,16),
Size(4,4),
10,
5);
imshow("hog visualization", r1);
waitKey(0);
return 0;
}
Mat get_hogdescriptor_visual_image(Mat& origImg,
vector< float>& descriptorValues,
Size winSize,
Size cellSize,
int scaleFactor,
double viz_factor)
{
Mat visual_image;
resize(origImg, visual_image, Size(origImg.cols*scaleFactor, origImg.rows*scaleFactor));
cvtColor(visual_image, visual_image, CV_GRAY2BGR);
int gradientBinSize = 9;
// dividing 180° into 9 bins, how large (in rad) is one bin?
float radRangeForOneBin = 3.14/(float)gradientBinSize;
// prepare data structure: 9 orientation / gradient strenghts for each cell
int cells_in_x_dir = winSize.width / cellSize.width;
int cells_in_y_dir = winSize.height / cellSize.height;
int totalnrofcells = cells_in_x_dir * cells_in_y_dir;
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;
}
}
// nr of blocks = nr of cells - 1
// since there is a new block on each cell (overlapping blocks!) but the last one
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;
} // 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]++;
} // 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 << "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(100,100,100),
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;
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(0,0,255),
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;
}
Wanted to say thanks for this code, still working beautifully 5 years later!!
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