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1 つのテンプレートを使用して複数のオブジェクトを照合するにはどうすればよいですか? 木の中心をテンプレートとして使用して、複数のバナナの木を一致させようとしています。私のプログラムは、航空写真のバナナの木のすべてのオカレンスと一致させたい 1 つのオカレンスのみを一致させています。

#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include <iostream>
#include <stdio.h>

using namespace std;
using namespace cv;

/// Global Variables
Mat img; Mat templ; Mat result;
const char* image_window = "Source Image";
const char* result_window = "Result window";

int match_method;
int max_Trackbar = 5;

/// Function Headers
void MatchingMethod( int, void* );

/**
 * @function main
 */
int main( int, char** argv )
{
  /// Load image and template
  img = imread( argv[1], 1 );
  templ = imread( argv[2], 1 );

  /// Create windows
  namedWindow( image_window, WINDOW_AUTOSIZE );
  namedWindow( result_window, WINDOW_AUTOSIZE );

  /// Create Trackbar
  const char* trackbar_label = "Method: \n 0: SQDIFF \n 1: SQDIFF NORMED \n 2: TM CCORR \n 3: TM CCORR NORMED \n 4: TM COEFF \n 5: TM COEFF NORMED";
  createTrackbar( trackbar_label, image_window, &match_method, max_Trackbar, MatchingMethod );

  MatchingMethod( 0, 0 );

  waitKey(0);
  return 0;
}

/**
 * @function MatchingMethod
 * @brief Trackbar callback
 */
void MatchingMethod( int, void* )
{
  /// Source image to display
  Mat img_display;
  img.copyTo( img_display );

  /// Create the result matrix
  int result_cols =  img.cols - templ.cols + 1;
  int result_rows = img.rows - templ.rows + 1;

  result.create( result_cols, result_rows, CV_32FC1 );

  /// Do the Matching and Normalize
  matchTemplate( img, templ, result, match_method );
  normalize( result, result, 0, 1, NORM_MINMAX, -1, Mat() );

  /// Localizing the best match with minMaxLoc
  double minVal; double maxVal; Point minLoc; Point maxLoc;
  Point matchLoc;

  minMaxLoc( result, &minVal, &maxVal, &minLoc, &maxLoc, Mat() );


  /// For SQDIFF and SQDIFF_NORMED, the best matches are lower values. For all the other methods, the higher the better
  if( match_method  == TM_SQDIFF || match_method == TM_SQDIFF_NORMED )
    { matchLoc = minLoc; }
  else
    { matchLoc = maxLoc; }

  /// Show me what you got
  rectangle( img_display, matchLoc, Point( matchLoc.x + templ.cols , matchLoc.y + templ.rows ), Scalar::all(0), 2, 8, 0 );
  rectangle( result, matchLoc, Point( matchLoc.x + templ.cols , matchLoc.y + templ.rows ), Scalar::all(0), 2, 8, 0 );

  imshow( image_window, img_display );
  imshow( result_window, result );

  return;
}

`

4

4 に答える 4

3

Saikat (および Bartlett) のコードでは、次のような行を使用しています。

result.at<float>(minLoc.x,minLoc.y)=1.0;

同様の行には次の欠点があります。コードは極値ピクセルのみをマスクし、次のループはおそらく同じオブジェクトを見つけて、1 ピクセルずらします。テンプレートサイズの長方形で結果をマスクすることをお勧めします。このコードにより、隣接するオブジェクトの重なり度合いを制御できます。

void matchingMethod(Mat& img,  const Mat& templ,  int     match_method)
{
    /// Source image to display
    Mat img_display; Mat result;
   if(img.channels()==3)
        cvtColor(img, img, cv::COLOR_BGR2GRAY);
    img.copyTo( img_display );//for later show off

    /// Create the result matrix - shows template responces
    int result_cols = img.cols - templ.cols + 1;
    int result_rows = img.rows - templ.rows + 1;
    result.create( result_cols, result_rows, CV_32FC1 );

    /// Do the Matching and Normalize
    matchTemplate( img, templ, result, match_method );
    normalize( result, result, 0, 1, NORM_MINMAX, -1, Mat() );

    /// Localizing the best match with minMaxLoc
    double minVal; double maxVal; 
    Point minLoc; Point maxLoc;
    Point matchLoc;


    //in my variant we create general initially positive mask 
    Mat general_mask=Mat::ones(result.rows,result.cols,CV_8UC1);

    for(int k=0;k<5;++k)// look for N=5 objects
    {
        minMaxLoc( result, &minVal, &maxVal, &minLoc, &maxLoc, general_mask); 
        //just to visually observe centering I stay this part of code:
        result.at<float>(minLoc ) =1.0;//
        result.at<float>(maxLoc ) =0.0;//

        // For SQDIFF and SQDIFF_NORMED, the best matches are lower values. 
         //For all the other methods, the higher the better
        if( match_method  == CV_TM_SQDIFF || match_method ==     CV_TM_SQDIFF_NORMED )
            matchLoc = minLoc;
        else
            matchLoc = maxLoc;
                                //koeffitient to control neiboring:
        //k_overlapping=1.- two neiboring selections can overlap half-body of     template
        //k_overlapping=2.- no overlapping,only border touching possible
        //k_overlapping>2.- distancing
        //0.< k_overlapping <1.-  selections can overlap more then half 
        float k_overlapping=1.7f;//little overlapping is good for my task

        //create template size for masking objects, which have been found,
        //to be excluded in the next loop run
        int template_w= ceil(k_overlapping*templ.cols);
        int template_h= ceil(k_overlapping*templ.rows);
        int x=matchLoc.x-template_w/2;
        int y=matchLoc.y-template_h/2;

        //shrink template-mask size to avoid boundary violation
        if(y<0) y=0;
        if(x<0) x=0;
        //will template come beyond the mask?:if yes-cut off margin; 
        if(template_w + x  > general_mask.cols) 
            template_w= general_mask.cols-x;
        if(template_h + y  > general_mask.rows) 
            template_h= general_mask.rows-y;

                               //set the negative mask to prevent repeating
        Mat template_mask=Mat::zeros(template_h,template_w, CV_8UC1);
        template_mask.copyTo(general_mask(cv::Rect(x, y, template_w, template_h)));

        /// Show me what you got on main image and on result (
        rectangle( img_display,matchLoc , Point( matchLoc.x + templ.cols ,    matchLoc.y + templ.rows ), Scalar::all(0), 2, 8, 0 );
        //small correction here-size of "result" is smaller
        rectangle( result,Point(matchLoc.x- templ.cols/2,matchLoc.y-     templ.rows/2) , Point( matchLoc.x + templ.cols/2 , matchLoc.y + templ.rows/2 ),     Scalar::all(0), 2, 8, 0 );
    }//for k= 0--5 
}
于 2015-02-21T16:13:37.760 に答える
1

メソッド CV_SQDIFF および CV_SQDIFF_NORMED の場合、最適な一致は最小値です。したがって、複数のオブジェクトを検出するには、最も小さい N 個の値を選択して表示します。ここで、N は表示するオブジェクトの数です。

他のすべての方法では、値が大きいほど一致が良好であることを表します。したがって、この場合、最大の N 個の値を選択します。

N は小さくする必要があります。そうしないと、間違った出力が得られます。

5 つのオブジェクトを検出するには、マッチング方法を次のように変更します。

void MatchingMethod( int, void* )
{
  /// Source image to display
  Mat img_display;
  img.copyTo( img_display );

  /// Create the result matrix
  int result_cols =  img.cols - templ.cols + 1;
  int result_rows = img.rows - templ.rows + 1;

  result.create( result_cols, result_rows, CV_32FC1 );

  /// Do the Matching and Normalize
  matchTemplate( img, templ, result, match_method );
  normalize( result, result, 0, 1, NORM_MINMAX, -1, Mat() );

  /// Localizing the best match with minMaxLoc
  Point minLoc; Point maxLoc;
  Point matchLoc;
  double minVal; double maxVal;

  for(int k=1;k<=5;k++)
  {
    minMaxLoc( result, &minVal, &maxVal, &minLoc, &maxLoc, Mat() );
    result.at<float>(minLoc.x,minLoc.y)=1.0;
    result.at<float>(maxLoc.x,maxLoc.y)=0.0;

  /// For SQDIFF and SQDIFF_NORMED, the best matches are lower values. For all the other methods, the higher the better
  if( match_method  == CV_TM_SQDIFF || match_method == CV_TM_SQDIFF_NORMED )
    { matchLoc = minLoc; }
  else
    { matchLoc = maxLoc; }

  /// Show me what you got
  rectangle( img_display, matchLoc, Point( matchLoc.x + templ.cols , matchLoc.y + templ.rows ), Scalar::all(0), 2, 8, 0 );
  rectangle( result, matchLoc, Point( matchLoc.x + templ.cols , matchLoc.y + templ.rows ), Scalar::all(0), 2, 8, 0 );
  }
  imshow( image_window, img_display );
  imshow( result_window, result );

  return;
}
于 2013-04-10T09:20:12.970 に答える
0

小さな間違い、以下に修正... (最低一致と書かれている部分)

void MatchingMethod( int, void* )
{
      /// Source image to display
      Mat img_display;
      img.copyTo( img_display );

      /// Create the result matrix
      int result_cols =  img.cols - templ.cols + 1;
      int result_rows = img.rows - templ.rows + 1;

      result.create( result_cols, result_rows, CV_32FC1 );

      /// Do the Matching and Normalize
      matchTemplate( img, templ, result, match_method );
      normalize( result, result, 0, 1, NORM_MINMAX, -1, Mat() );

      /// Localizing the best match with minMaxLoc
      Point minLoc; Point maxLoc;
      Point matchLoc;
      double minVal; double maxVal;

      for(int k=1;k<=5;k++)
      {
        minMaxLoc( result, &minVal, &maxVal, &minLoc, &maxLoc, Mat() );

        // Lowest matches
        if( match_method  == CV_TM_SQDIFF || match_method == CV_TM_SQDIFF_NORMED )
        {
            result.at<float>(minLoc.x,minLoc.y)=1.0;
            result.at<float>(maxLoc.x,maxLoc.y)=1.0;
        }
        else
        {
            result.at<float>(minLoc.x,minLoc.y)=0.0;
            result.at<float>(maxLoc.x,maxLoc.y)=0.0;
        }

      /// For SQDIFF and SQDIFF_NORMED, the best matches are lower values. For all the other methods, the higher the better
      if( match_method  == CV_TM_SQDIFF || match_method == CV_TM_SQDIFF_NORMED )
        { matchLoc = minLoc; }
      else
        { matchLoc = maxLoc; }

      /// Show me what you got
      rectangle( img_display, matchLoc, Point( matchLoc.x + templ.cols , matchLoc.y + templ.rows ), Scalar::all(0), 2, 8, 0 );
      rectangle( result, matchLoc, Point( matchLoc.x + templ.cols , matchLoc.y + templ.rows ), Scalar::all(0), 2, 8, 0 );
      }
      imshow( image_window, img_display );
      imshow( result_window, result );

      return;
    }
于 2014-07-19T07:47:50.077 に答える
0

最小または最大の Mat 結果を手動で検索します - 使用した方法で変更します - 値が一致する場合は座標を取得します

于 2013-04-10T09:11:41.340 に答える