Goal

In this tutorial you will learn how to:

Use the OpenCV function matchTemplate() to search for matches between an image patch and an input image
Use the OpenCV function minMaxLoc() to find the maximum and minimum values (as well as their positions) in a given array.

Theory

What is template matching?

Template matching is a technique for finding areas of an image that match (are similar) to a template image (patch).

While the patch must be a rectangle it may be that not all of the rectangle is relevant. In such a case, a mask can be used to isolate the portion of the patch that should be used to find the match.

How does it work?

We need two primary components:
1. Source image (I): The image in which we expect to find a match to the template image
2. Template image (T): The patch image which will be compared to the source image
our goal is to detect the highest matching area:

To identify the matching area, we have to compare the template image against the source image by sliding it:

By sliding, we mean moving the patch one pixel at a time (left to right, up to down). At each location, a metric is calculated so it represents how "good" or "bad" the match at that location is (or how similar the patch is to that particular area of the source image).
For each location of T over I, you store the metric in the result matrix R. Each location \((x,y)\) in R contains the match metric:

the image above is the result R of sliding the patch with a metric TM_CCORR_NORMED. The brightest locations indicate the highest matches. As you can see, the location marked by the red circle is probably the one with the highest value, so that location (the rectangle formed by that point as a corner and width and height equal to the patch image) is considered the match.

In practice, we locate the highest value (or lower, depending of the type of matching method) in the R matrix, using the function minMaxLoc()

How does the mask work?

If masking is needed for the match, three components are required:
1. Source image (I): The image in which we expect to find a match to the template image
2. Template image (T): The patch image which will be compared to the source image
3. Mask image (M): The mask, a grayscale image that masks the template
Only two matching methods currently accept a mask: TM_SQDIFF and TM_CCORR_NORMED (see below for explanation of all the matching methods available in opencv).
The mask must have the same dimensions as the template
The mask should have a CV_8U or CV_32F depth and the same number of channels as the template image. In CV_8U case, the mask values are treated as binary, i.e. zero and non-zero. In CV_32F case, the values should fall into [0..1] range and the template pixels will be multiplied by the corresponding mask pixel values. Since the input images in the sample have the CV_8UC3 type, the mask is also read as color image.

Which are the matching methods available in OpenCV?

Good question. OpenCV implements Template matching in the function matchTemplate(). The available methods are 6:

method=TM_SQDIFF

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

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

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

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

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

where

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

What does this program do?
- Loads an input image, an image patch (template), and optionally a mask
- Perform a template matching procedure by using the OpenCV function matchTemplate() with any of the 6 matching methods described before. The user can choose the method by entering its selection in the Trackbar. If a mask is supplied, it will only be used for the methods that support masking
- Normalize the output of the matching procedure
- Localize the location with higher matching probability
- Draw a rectangle around the area corresponding to the highest match

Downloadable code: Click here
Code at glance:

#include "opencv2/imgcodecs.hpp"

#include "opencv2/highgui.hpp"

#include "opencv2/imgproc.hpp"

#include <iostream>

using namespace std;

using namespace cv;

bool use_mask;

Mat img; Mat templ; Mat mask; Mat result;

const char* image_window = "Source Image";

const char* result_window = "Result window";

int match_method;

int max_Trackbar = 5;

void MatchingMethod( int, void* );

const char* keys =

"{ help h| | Print help message. }"

"{ @input1 | Template_Matching_Original_Image.jpg | image_name }"

"{ @input2 | Template_Matching_Template_Image.jpg | template_name }"

"{ @input3 | | mask_name }";

int main( int argc, char** argv )

{

CommandLineParser parser( argc, argv, keys );

samples::addSamplesDataSearchSubDirectory( "doc/tutorials/imgproc/histograms/template_matching/images" );

img = imread( samples::findFile( parser.get<String>("@input1") ) );

templ = imread( samples::findFile( parser.get<String>("@input2") ), IMREAD_COLOR );

if(argc > 3) {

use_mask = true;

mask = imread(samples::findFile( parser.get<String>("@input3") ), IMREAD_COLOR );

}

if(img.empty() || templ.empty() || (use_mask && mask.empty()))

{

cout << "Can't read one of the images" << endl;

return EXIT_FAILURE;

}

namedWindow( image_window, WINDOW_AUTOSIZE );

namedWindow( result_window, WINDOW_AUTOSIZE );

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 EXIT_SUCCESS;

}

void MatchingMethod( int, void* )

{

Mat img_display;

img.copyTo( img_display );

int result_cols = img.cols - templ.cols + 1;

int result_rows = img.rows - templ.rows + 1;

result.create( result_rows, result_cols, CV_32FC1 );

bool method_accepts_mask = (TM_SQDIFF == match_method || match_method == TM_CCORR_NORMED);

if (use_mask && method_accepts_mask)

{ matchTemplate( img, templ, result, match_method, mask); }

else

{ matchTemplate( img, templ, result, match_method); }

normalize( result, result, 0, 1, NORM_MINMAX, -1, Mat() );

double minVal; double maxVal; Point minLoc; Point maxLoc;

Point matchLoc;

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

if( match_method == TM_SQDIFF || match_method == TM_SQDIFF_NORMED )

{ matchLoc = minLoc; }

else

{ matchLoc = maxLoc; }

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;

}

cv::CommandLineParser
Designed for command line parsing.
Definition utility.hpp:890

cv::Mat
n-dimensional dense array class
Definition mat.hpp:828

cv::Mat::copyTo
void copyTo(OutputArray m) const
Copies the matrix to another one.

cv::Mat::create
void create(int rows, int cols, int type)
Allocates new array data if needed.

cv::Mat::cols
int cols
Definition mat.hpp:2154

cv::Mat::empty
bool empty() const
Returns true if the array has no elements.

cv::Mat::rows
int rows
the number of rows and columns or (-1, -1) when the matrix has more than 2 dimensions
Definition mat.hpp:2154

cv::Point_< int >

cv::Point_::y
_Tp y
y coordinate of the point
Definition types.hpp:202

cv::Point_::x
_Tp x
x coordinate of the point
Definition types.hpp:201

cv::normalize
void normalize(InputArray src, InputOutputArray dst, double alpha=1, double beta=0, int norm_type=NORM_L2, int dtype=-1, InputArray mask=noArray())
Normalizes the norm or value range of an array.

cv::minMaxLoc
void minMaxLoc(InputArray src, double *minVal, double *maxVal=0, Point *minLoc=0, Point *maxLoc=0, InputArray mask=noArray())
Finds the global minimum and maximum in an array.

cv::String
std::string String
Definition cvstd.hpp:151

CV_32FC1
#define CV_32FC1
Definition interface.h:118

cv::imshow
void imshow(const String &winname, InputArray mat)
Displays an image in the specified window.

cv::rectangle
void rectangle(InputOutputArray img, Point pt1, Point pt2, const Scalar &color, int thickness=1, int lineType=LINE_8, int shift=0)
Draws a simple, thick, or filled up-right rectangle.

cv::matchTemplate
void matchTemplate(InputArray image, InputArray templ, OutputArray result, int method, InputArray mask=noArray())
Compares a template against overlapped image regions.

highgui.hpp

main
int main(int argc, char *argv[])
Definition highgui_qt.cpp:3

imgcodecs.hpp

imgproc.hpp

cv
Definition core.hpp:107

std
STL namespace.

Downloadable code: Click here
Code at glance:
import java.awt.GridLayout;

import java.awt.Image;

import java.util.Hashtable;

import javax.swing.ImageIcon;

import javax.swing.JFrame;

import javax.swing.JLabel;

import javax.swing.JSlider;

import javax.swing.event.ChangeEvent;

import javax.swing.event.ChangeListener;

import org.opencv.core.Core;

import org.opencv.core.CvType;

import org.opencv.core.Mat;

import org.opencv.core.Point;

import org.opencv.core.Scalar;

import org.opencv.highgui.HighGui;

import org.opencv.imgcodecs.Imgcodecs;

import org.opencv.imgproc.Imgproc;

class MatchTemplateDemoRun implements ChangeListener {

Boolean use_mask = false;

Mat img = new Mat(), templ = new Mat();

Mat mask = new Mat();

int match_method;

JLabel imgDisplay = new JLabel(), resultDisplay = new JLabel();

public void run(String[] args) {

if (args.length < 2) {

System.out.println("Not enough parameters");

System.out.println("Program arguments:\n<image_name> <template_name> [<mask_name>]");

System.exit(-1);

}

img = Imgcodecs.imread(args[0], Imgcodecs.IMREAD_COLOR);

templ = Imgcodecs.imread(args[1], Imgcodecs.IMREAD_COLOR);

if (args.length > 2) {

use_mask = true;

mask = Imgcodecs.imread(args[2], Imgcodecs.IMREAD_COLOR);

}

if (img.empty() || templ.empty() || (use_mask && mask.empty())) {

System.out.println("Can't read one of the images");

System.exit(-1);

}

matchingMethod();

createJFrame();

}

private void matchingMethod() {

Mat result = new Mat();

Mat img_display = new Mat();

img.copyTo(img_display);

int result_cols = img.cols() - templ.cols() + 1;

int result_rows = img.rows() - templ.rows() + 1;

result.create(result_rows, result_cols, CvType.CV_32FC1);

Boolean method_accepts_mask = (Imgproc.TM_SQDIFF == match_method || match_method == Imgproc.TM_CCORR_NORMED);

if (use_mask && method_accepts_mask) {

Imgproc.matchTemplate(img, templ, result, match_method, mask);

} else {

Imgproc.matchTemplate(img, templ, result, match_method);

}

Core.normalize(result, result, 0, 1, Core.NORM_MINMAX, -1, new Mat());

Point matchLoc;

Core.MinMaxLocResult mmr = Core.minMaxLoc(result);

if (match_method == Imgproc.TM_SQDIFF || match_method == Imgproc.TM_SQDIFF_NORMED) {

matchLoc = mmr.minLoc;

} else {

matchLoc = mmr.maxLoc;

}

Imgproc.rectangle(img_display, matchLoc, new Point(matchLoc.x + templ.cols(), matchLoc.y + templ.rows()),

new Scalar(0, 0, 0), 2, 8, 0);

Imgproc.rectangle(result, matchLoc, new Point(matchLoc.x + templ.cols(), matchLoc.y + templ.rows()),

new Scalar(0, 0, 0), 2, 8, 0);

Image tmpImg = HighGui.toBufferedImage(img_display);

ImageIcon icon = new ImageIcon(tmpImg);

imgDisplay.setIcon(icon);

result.convertTo(result, CvType.CV_8UC1, 255.0);

tmpImg = HighGui.toBufferedImage(result);

icon = new ImageIcon(tmpImg);

resultDisplay.setIcon(icon);

}

@Override

public void stateChanged(ChangeEvent e) {

JSlider source = (JSlider) e.getSource();

if (!source.getValueIsAdjusting()) {

match_method = source.getValue();

matchingMethod();

}

}

private void createJFrame() {

String title = "Source image; Control; Result image";

JFrame frame = new JFrame(title);

frame.setLayout(new GridLayout(2, 2));

frame.add(imgDisplay);

int min = 0, max = 5;

JSlider slider = new JSlider(JSlider.VERTICAL, min, max, match_method);

slider.setPaintTicks(true);

slider.setPaintLabels(true);

// Set the spacing for the minor tick mark

slider.setMinorTickSpacing(1);

// Customizing the labels

Hashtable<Integer, JLabel> labelTable = new Hashtable<>();

labelTable.put(new Integer(0), new JLabel("0 - SQDIFF"));

labelTable.put(new Integer(1), new JLabel("1 - SQDIFF NORMED"));

labelTable.put(new Integer(2), new JLabel("2 - TM CCORR"));

labelTable.put(new Integer(3), new JLabel("3 - TM CCORR NORMED"));

labelTable.put(new Integer(4), new JLabel("4 - TM COEFF"));

labelTable.put(new Integer(5), new JLabel("5 - TM COEFF NORMED : (Method)"));

slider.setLabelTable(labelTable);

slider.addChangeListener(this);

frame.add(slider);

frame.add(resultDisplay);

frame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);

frame.pack();

frame.setVisible(true);

}

}

public class MatchTemplateDemo {

public static void main(String[] args) {

// load the native OpenCV library

System.loadLibrary(Core.NATIVE_LIBRARY_NAME);

// run code

new MatchTemplateDemoRun().run(args);

}

}

cv::min
void min(InputArray src1, InputArray src2, OutputArray dst)
Calculates per-element minimum of two arrays or an array and a scalar.

cv::max
void max(InputArray src1, InputArray src2, OutputArray dst)
Calculates per-element maximum of two arrays or an array and a scalar.

cv::Point
Point2i Point
Definition types.hpp:209

cv::Scalar
Scalar_< double > Scalar
Definition types.hpp:709

Downloadable code: Click here
Code at glance:
from __future__ import print_function

import sys

import cv2 as cv

use_mask = False

img = None

templ = None

mask = None

image_window = "Source Image"

result_window = "Result window"

match_method = 0

max_Trackbar = 5

def main(argv):

if (len(sys.argv) < 3):

print('Not enough parameters')

print('Usage:\nmatch_template_demo.py <image_name> <template_name> [<mask_name>]')

return -1

global img

global templ

img = cv.imread(sys.argv[1], cv.IMREAD_COLOR)

templ = cv.imread(sys.argv[2], cv.IMREAD_COLOR)

if (len(sys.argv) > 3):

global use_mask

use_mask = True

global mask

mask = cv.imread( sys.argv[3], cv.IMREAD_COLOR )

if ((img is None) or (templ is None) or (use_mask and (mask is None))):

print('Can\'t read one of the images')

return -1

cv.namedWindow( image_window, cv.WINDOW_AUTOSIZE )

cv.namedWindow( result_window, cv.WINDOW_AUTOSIZE )

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'

cv.createTrackbar( trackbar_label, image_window, match_method, max_Trackbar, MatchingMethod )

MatchingMethod(match_method)

cv.waitKey(0)

return 0

def MatchingMethod(param):

global match_method

match_method = param

img_display = img.copy()

method_accepts_mask = (cv.TM_SQDIFF == match_method or match_method == cv.TM_CCORR_NORMED)

if (use_mask and method_accepts_mask):

result = cv.matchTemplate(img, templ, match_method, None, mask)

else:

result = cv.matchTemplate(img, templ, match_method)

cv.normalize( result, result, 0, 1, cv.NORM_MINMAX, -1 )

_minVal, _maxVal, minLoc, maxLoc = cv.minMaxLoc(result, None)

if (match_method == cv.TM_SQDIFF or match_method == cv.TM_SQDIFF_NORMED):

matchLoc = minLoc

else:

matchLoc = maxLoc

cv.rectangle(img_display, matchLoc, (matchLoc[0] + templ.shape[0], matchLoc[1] + templ.shape[1]), (0,0,0), 2, 8, 0 )

cv.rectangle(result, matchLoc, (matchLoc[0] + templ.shape[0], matchLoc[1] + templ.shape[1]), (0,0,0), 2, 8, 0 )

cv.imshow(image_window, img_display)

cv.imshow(result_window, result)

pass

if __name__ == "__main__":

main(sys.argv[1:])

cv::waitKey
int waitKey(int delay=0)
Waits for a pressed key.

cv::namedWindow
void namedWindow(const String &winname, int flags=WINDOW_AUTOSIZE)
Creates a window.

cv::createTrackbar
int createTrackbar(const String &trackbarname, const String &winname, int *value, int count, TrackbarCallback onChange=0, void *userdata=0)
Creates a trackbar and attaches it to the specified window.

cv::imread
CV_EXPORTS_W Mat imread(const String &filename, int flags=IMREAD_COLOR_BGR)
Loads an image from a file.

Explanation

Declare some global variables, such as the image, template and result matrices, as well as the match method and the window names:

C++

bool use_mask;

Mat img; Mat templ; Mat mask; Mat result;

const char* image_window = "Source Image";

const char* result_window = "Result window";

int match_method;

int max_Trackbar = 5;

Java

Boolean use_mask = false;

Mat img = new Mat(), templ = new Mat();

Mat mask = new Mat();

int match_method;

JLabel imgDisplay = new JLabel(), resultDisplay = new JLabel();

Python

use_mask = False

img = None

templ = None

mask = None

image_window = "Source Image"

result_window = "Result window"

match_method = 0

max_Trackbar = 5
Load the source image, template, and optionally, if supported for the matching method, a mask:

C++

img = imread( samples::findFile( parser.get<String>("@input1") ) );

templ = imread( samples::findFile( parser.get<String>("@input2") ), IMREAD_COLOR );

if(argc > 3) {

use_mask = true;

mask = imread(samples::findFile( parser.get<String>("@input3") ), IMREAD_COLOR );

}

if(img.empty() || templ.empty() || (use_mask && mask.empty()))

{

cout << "Can't read one of the images" << endl;

return EXIT_FAILURE;

}

Java

img = Imgcodecs.imread(args[0], Imgcodecs.IMREAD_COLOR);

templ = Imgcodecs.imread(args[1], Imgcodecs.IMREAD_COLOR);

Python

global img

global templ

img = cv.imread(sys.argv[1], cv.IMREAD_COLOR)

templ = cv.imread(sys.argv[2], cv.IMREAD_COLOR)

if (len(sys.argv) > 3):

global use_mask

use_mask = True

global mask

mask = cv.imread( sys.argv[3], cv.IMREAD_COLOR )

if ((img is None) or (templ is None) or (use_mask and (mask is None))):

print('Can\'t read one of the images')

return -1
Create the Trackbar to enter the kind of matching method to be used. When a change is detected the callback function is called.

C++

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 );

Java

int min = 0, max = 5;

JSlider slider = new JSlider(JSlider.VERTICAL, min, max, match_method);

Python

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'

cv.createTrackbar( trackbar_label, image_window, match_method, max_Trackbar, MatchingMethod )
Let's check out the callback function. First, it makes a copy of the source image:

C++

Mat img_display;

img.copyTo( img_display );

Java

Mat img_display = new Mat();

img.copyTo(img_display);

Python

img_display = img.copy()
Perform the template matching operation. The arguments are naturally the input image I, the template T, the result R and the match_method (given by the Trackbar), and optionally the mask image M.

C++

bool method_accepts_mask = (TM_SQDIFF == match_method || match_method == TM_CCORR_NORMED);

if (use_mask && method_accepts_mask)

{ matchTemplate( img, templ, result, match_method, mask); }

else

{ matchTemplate( img, templ, result, match_method); }

Java

Boolean method_accepts_mask = (Imgproc.TM_SQDIFF == match_method || match_method == Imgproc.TM_CCORR_NORMED);

if (use_mask && method_accepts_mask) {

Imgproc.matchTemplate(img, templ, result, match_method, mask);

} else {

Imgproc.matchTemplate(img, templ, result, match_method);

}

Python

method_accepts_mask = (cv.TM_SQDIFF == match_method or match_method == cv.TM_CCORR_NORMED)

if (use_mask and method_accepts_mask):

result = cv.matchTemplate(img, templ, match_method, None, mask)

else:

result = cv.matchTemplate(img, templ, match_method)
We normalize the results:

C++

normalize( result, result, 0, 1, NORM_MINMAX, -1, Mat() );

Java

Core.normalize(result, result, 0, 1, Core.NORM_MINMAX, -1, new Mat());

Python

cv.normalize( result, result, 0, 1, cv.NORM_MINMAX, -1 )
We localize the minimum and maximum values in the result matrix R by using minMaxLoc().

C++

double minVal; double maxVal; Point minLoc; Point maxLoc;

Point matchLoc;

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

Java

Point matchLoc;

Core.MinMaxLocResult mmr = Core.minMaxLoc(result);

Python

_minVal, _maxVal, minLoc, maxLoc = cv.minMaxLoc(result, None)
For the first two methods ( TM_SQDIFF and MT_SQDIFF_NORMED ) the best match are the lowest values. For all the others, higher values represent better matches. So, we save the corresponding value in the matchLoc variable:

C++

if( match_method == TM_SQDIFF || match_method == TM_SQDIFF_NORMED )

{ matchLoc = minLoc; }

else

{ matchLoc = maxLoc; }

Java

if (match_method == Imgproc.TM_SQDIFF || match_method == Imgproc.TM_SQDIFF_NORMED) {

matchLoc = mmr.minLoc;

} else {

matchLoc = mmr.maxLoc;

}

Python

if (match_method == cv.TM_SQDIFF or match_method == cv.TM_SQDIFF_NORMED):

matchLoc = minLoc

else:

matchLoc = maxLoc
Display the source image and the result matrix. Draw a rectangle around the highest possible matching area:

C++

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 );

Java

Imgproc.rectangle(img_display, matchLoc, new Point(matchLoc.x + templ.cols(), matchLoc.y + templ.rows()),

new Scalar(0, 0, 0), 2, 8, 0);

Imgproc.rectangle(result, matchLoc, new Point(matchLoc.x + templ.cols(), matchLoc.y + templ.rows()),

new Scalar(0, 0, 0), 2, 8, 0);

Image tmpImg = HighGui.toBufferedImage(img_display);

ImageIcon icon = new ImageIcon(tmpImg);

imgDisplay.setIcon(icon);

result.convertTo(result, CvType.CV_8UC1, 255.0);

tmpImg = HighGui.toBufferedImage(result);

icon = new ImageIcon(tmpImg);

resultDisplay.setIcon(icon);

Python

cv.rectangle(img_display, matchLoc, (matchLoc[0] + templ.shape[0], matchLoc[1] + templ.shape[1]), (0,0,0), 2, 8, 0 )

cv.rectangle(result, matchLoc, (matchLoc[0] + templ.shape[0], matchLoc[1] + templ.shape[1]), (0,0,0), 2, 8, 0 )

cv.imshow(image_window, img_display)

cv.imshow(result_window, result)

Results

Testing our program with an input image such as:

and a template image:

Generate the following result matrices (first row are the standard methods SQDIFF, CCORR and CCOEFF, second row are the same methods in its normalized version). In the first column, the darkest is the better match, for the other two columns, the brighter a location, the higher the match.
The right match is shown below (black rectangle around the face of the guy at the right). Notice that CCORR and CCDEFF gave erroneous best matches, however their normalized version did it right, this may be due to the fact that we are only considering the "highest match" and not the other possible high matches.


Original author	Ana Huamán
Compatibility	OpenCV >= 3.0

Table of Contents