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#include #include #include #include #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 & 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 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;}
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