opencv matchTemplate函数的解析

opencv有个MatchTemplate_Demo.cpp文件实现了matchTemplate这个函数的调用demo。

首先这个函数的功能是根据一个小块物体的图片，然后在整幅图里面搜索和他最像的区域。

算法在他的官方文档上讲的很清楚了，一共有六种方法：

1. method=CV_TM_SQDIFF

   

2. method=CV_TM_SQDIFF_NORMED

   

3. method=CV_TM_CCORR

   

4. method=CV_TM_CCORR_NORMED

   

5. method=CV_TM_CCOEFF

   

   where

   

6. method=CV_TM_CCOEFF_NORMED

   

a最明显，就是遍历每个像素，对于单个像素，像其有下角画框，框的大小就是要找的样例的大小，我们称之为template。对于框内的每个像素值和template的像素值逐点作差然后平方求和，类似一个卷积的过程。所以最终自然是越小越像咯，所以在matchTemplate这个函数的result图像中最小的那个点就是最像的区域。对于之后的五种方法类似，x'就是template的坐标，实际上也就是一个浮动位。

所以最终最像的结果依次是

a:最小值 b:最小值 c:最大值 d:最大值 e:最大值 f:最大值

再看demo的代码：

#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( argv[1], 1 );  templ = imread( argv[2], 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 );  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_rows, result_cols, 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  == 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;}

其中承载的有3个Mat ，img是大图，templ是要找的小块，就是要在img上找和templ最像的区域。result就是返回计算结果，他的大小是img-templ，因为img上每个点找templ的结果，所以最右和最下的像素到边界的距离小于templ的大小了，所以就不找了。所以result上的每个点的像素值就是相似度了，具体是越大越像还是越小越像参照上面的解说。

所以demo用了

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  == CV_TM_SQDIFF || match_method == CV_TM_SQDIFF_NORMED )    { matchLoc = minLoc; }  else    { matchLoc = maxLoc; }

来找最像的那个区域左上角的点

然后往下画矩形就是最像的那个区域了。

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 );