'opencv'에 해당되는 글 1건

OpenCV에 포함되어 있는 기본적인 HOG 특징을 뽑아주는 함수이다.

기본 사람검출을 위한 파라미터를 이용해서 간단한 테스트만 해봤다.

나중에 이걸 쓸일이 있을지는 모르겠지만 일단 저장..

아마 다음에 hog를 쓴다면 아마 구현해서 쓰겠지... 

1. Mat img;
2.  
3. img = imread( "C:\\Users\\Administrator\\Pictures\\test.jpg" );
4.  
5. HOGDescriptor hog;
6. hog.setSVMDetector( HOGDescriptor::getDefaultPeopleDetector() );
7. vector<Rect> detected;
8.  
9. double time = double( GetTickCount() );
10.  
11. hog.detectMultiScale( img, detected, 0, Size(9,9), Size(26,20), 1.05, 2 );
12. time = double( GetTickCount() ) - time;
13.  
14. int detectedCNT = detected.size();
15.  
16. for( int i=0; i<detectedCNT; i++ )
17. {
18.         Rect people = detected[i];
19.         rectangle( img, people, Scalar(0,0,255), 2 );
20. }
21.  
22. imshow( "Result", img )

그래도 별다른 작업없이 기본 파라미터만 이용해서 

사검출 결과가 나오니 간단한 테스트용으로는 사용 할 만 하겠지?

아래사진은 작년 경주에서 찍은 사진.. 옆에 형은 초상권.. 으로인한 모자이크 처리 해드립니다.

성능은 뭐 예상대로 별로다. 어거지로 끼워 맞춘 느낌.. 

아래는 외국사이트에서 본 hog feature 시각화해서 보여주는 소스란다.

아직 확인은 안해봤지만 나중에 참고하도록 하자.

// 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;
 
}