用OpenCV顯示HOG特徵圖的C++程式碼
阿新 • • 發佈:2018-11-25
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特徵向量 Size winSize,//圖片視窗大小 Size cellSize, int scaleFactor,//縮放背景影象的比例 double viz_factor)//縮放hog特徵的線長比例 { Mat visual_image;//最後視覺化的影象大小 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對應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個數 int cells_in_y_dir = winSize.height / cellSize.height;//y方向上的cell個數 int totalnrofcells = cells_in_x_dir * cells_in_y_dir;//cell的總個數 //注意此處三維陣列的定義格式 //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;//把每個cell的9個bin對應的梯度強度都初始化為0 } } // nr of blocks = nr of cells - 1 // since there is a new block on each cell (overlapping blocks!) but the last one //相當於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;//因為C是按行儲存 } // 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]++;//由於block之間有重疊,所以要記錄哪些cell被多次計算了 } // 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框線的顏色 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;//取每個bin裡的中間值,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視覺化的cell的顏色 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;//返回最終的HOG視覺化影象 } int main() { HOGDescriptor hog;//使用的是預設的hog引數 /* 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. */ //對於128*80的圖片,blockstride = 8,15*9的block,2*2*9*15*9 = 4860 Mat src = imread("E:\\C++WorkPlace\\myHOGvisualize\\myHOGvisualize\\img.jpg");//注意這裡邊的雙斜槓!!!!!!!!!! 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特徵向量 Mat dst; resize(src, dst, Size(width, height));//規範影象尺寸 imshow("src", src); hog.compute(dst,des);//計算hog特徵 Mat background = Mat::zeros(Size(width,height),CV_8UC1);//設定黑色背景圖,因為要用白色繪製hog特徵 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; }