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OpenCV Tutorials —— Template Matching

2024-08-03 22:30:19   221人阅读

模板匹配

从源图像中发掘目标图像

将目标图像块逐像素滑动，然后度量此区域的源图像块和目标图像块的匹配程度

匹配都最高的像素位置作为最终定位

 

void matchTemplate(InputArray image, InputArray templ, OutputArray result, int method)

度量手段 —— 相关性

  1. method=CV_TM_SQDIFF

    R(x,y)= \sum _{x',y'} (T(x',y')-I(x+x',y+y'))^2

  2. method=CV_TM_SQDIFF_NORMED

    R(x,y)= \frac{\sum_{x',y'} (T(x',y')-I(x+x',y+y'))^2}{\sqrt{\sum_{x',y'}T(x',y')^2 \cdot \sum_{x',y'} I(x+x',y+y')^2}}

  3. method=CV_TM_CCORR

    R(x,y)= \sum _{x',y'} (T(x',y') \cdot I(x+x',y+y'))

  4. method=CV_TM_CCORR_NORMED

    R(x,y)= \frac{\sum_{x',y'} (T(x',y') \cdot I(x+x',y+y'))}{\sqrt{\sum_{x',y'}T(x',y')^2 \cdot \sum_{x',y'} I(x+x',y+y')^2}}

  5. method=CV_TM_CCOEFF

    R(x,y)= \sum _{x',y'} (T'(x',y') \cdot I(x+x',y+y'))

    where

    \begin{array}{l} T'(x',y')=T(x',y') - 1/(w \cdot h) \cdot \sum _{x'',y''} T(x'',y'') \\ I'(x+x',y+y')=I(x+x',y+y') - 1/(w \cdot h) \cdot \sum _{x'',y''} I(x+x'',y+y'') \end{array}

  6. method=CV_TM_CCOEFF_NORMED

    R(x,y)= \frac{ \sum_{x',y'} (T'(x',y') \cdot I'(x+x',y+y')) }{ \sqrt{\sum_{x',y'}T'(x',y')^2 \cdot \sum_{x',y'} I'(x+x',y+y')^2} }

 

Code

#include "stdafx.h"#include "opencv2/highgui/highgui.hpp"#include "opencv2/imgproc/imgproc.hpp"#include <iostream>#include <stdio.h>using namespace std;using namespace cv;/// Global VariablesMat img; Mat templ; Mat result;char* image_window = "Source Image";char* result_window = "Result window";int match_method;int max_Trackbar = 5;/// Function Headersvoid MatchingMethod( int, void* );/** @function main */int main( int argc, char** argv ){  /// Load image and template  img = imread( "dashu.jpg", 1 );  templ = imread( "temp.jpg", 1 );  /// Create windows  namedWindow( image_window, CV_WINDOW_AUTOSIZE );  namedWindow( result_window, CV_WINDOW_AUTOSIZE );  /// Create Trackbar  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 );	// opencv 没有按钮，拿滑动条将就  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() );	// 归一化  /// Localizing the best match with minMaxLoc  double minVal; double maxVal; Point minLoc; Point maxLoc;  Point matchLoc;  minMaxLoc( result, &minVal, &maxVal, &minLoc, &maxLoc, Mat() );	// 返回最小值最大值点位置  /// 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;}

OpenCV Tutorials —— Template Matching


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