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:
-
method=CV_TM_SQDIFF
-
method=CV_TM_SQDIFF_NORMED
-
method=CV_TM_CCORR
-
method=CV_TM_CCORR_NORMED
-
method=CV_TM_CCOEFF
where
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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 Variables
Mat img; Mat templ; Mat result;
char* image_window = "Source Image";
char* result_window = "Result window";
int match_method;
int max_Trackbar = 5;
/// Function Headers
void 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 );