使用单个模板匹配多个对象

Question

如何使用单个模板匹配多个对象？我试图使用树的中心作为模板匹配多个香蕉树。我的程序只匹配我希望匹配航拍图像中香蕉树的所有出现的一个出现。

#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;
}

`

Answer 1

在Saikat（和Bartlett's）的代码中使用诸如

之类的行

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

和类似的行 有下一个缺点：代码屏蔽了唯一的极值像素和下一个循环 可能会找到相同的对象，将一个像素移到一边。 我建议用模板大小的矩形来掩盖结果。此代码启用 控制相邻物体的重叠程度。

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 
}

Answer 2

对于方法CV_SQDIFF和CV_SQDIFF_NORMED，最佳匹配是最低值。因此，要检测多个对象，请选择最少N个值并显示它们，其中N是您要显示的对象数。

对于所有其他方法，较高的值表示更好的匹配。所以在这种情况下，选择最多N个值。

N必须小，否则你的输出会出错。

要检测5个对象，请按以下步骤更改匹配方法

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;
}

Answer 3

手动搜索最小或最大的Mat结果 - 使用已使用的方法更改 - 如果匹配vaule获取坐标

Answer 4

小错误，在下面更正...（它说的是最低匹配的位）

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;
    }