OpenCV  3.4.8-pre
Open Source Computer Vision
Template Matching

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Goal

In this tutorial you will learn how to:

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?

How does the mask work?

Which are the matching methods available in OpenCV?

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

  1. method=TM_SQDIFF

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

  2. method=TM_SQDIFF_NORMED

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

  3. method=TM_CCORR

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

  4. method=TM_CCORR_NORMED

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

  5. method=TM_CCOEFF

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

    where

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

  6. 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

Explanation

Results

  1. Testing our program with an input image such as:

    Template_Matching_Original_Image.jpg

    and a template image:

    Template_Matching_Template_Image.jpg
  2. 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.
    Template_Matching_Correl_Result_0.jpg
    Result_0
    Template_Matching_Correl_Result_1.jpg
    Result_1
    Template_Matching_Correl_Result_2.jpg
    Result_2
    Template_Matching_Correl_Result_3.jpg
    Result_3
    Template_Matching_Correl_Result_4.jpg
    Result_4
    Template_Matching_Correl_Result_5.jpg
    Result_5
  3. 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.

    Template_Matching_Image_Result.jpg