HOG特征可视化

可视化说明

$$\frac{900-16}{8}=110.5$$

$$\frac{904-16}{8}=111$$

代码实现

#include <iostream> #include <opencv2/core/core.hpp> #include <opencv2/highgui/highgui.hpp> #include <opencv2/opencv.hpp> using namespace cv; using namespace std; // HOGDescriptor visual_imagealizer  // adapted for arbitrary size of feature sets and training images  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)); 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; } int main() { HOGDescriptor hog; hog.winSize=Size(904,600); vector<float> des; Mat src = imread("timg.jpg"); Mat dst ; resize(src,dst,Size(904,600)); imshow("src",src); hog.compute(dst,des); cout<<des.size()<<endl; Mat background = Mat::zeros(Size(904,600),CV_8UC1); Mat background_hog = get_hogdescriptor_visual_image(background,des,hog.winSize,hog.cellSize,1,2.0); imshow("HOG特征1",background_hog); imwrite("特征可视化1.jpg",background_hog); Mat src_hog = get_hogdescriptor_visual_image(src,des,hog.winSize,hog.cellSize,1,2.0); imshow("HOG特征2",src_hog); imwrite("特征可视化2.jpg",src_hog); waitKey(); return 0; }