Object Detection


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MatchTemplate(image, templ, result, method) → None

Compares a template against overlapped image regions.

  • image (CvArr) – Image where the search is running; should be 8-bit or 32-bit floating-point
  • templ (CvArr) – Searched template; must be not greater than the source image and the same data type as the image
  • result (CvArr) – A map of comparison results; single-channel 32-bit floating-point. If image is W \times H and templ is w \times h then result must be (W-w+1) \times (H-h+1)
  • method (int) – Specifies the way the template must be compared with the image regions (see below)

The function is similar to CalcBackProjectPatch . It slides through image , compares the overlapped patches of size w \times h against templ using the specified method and stores the comparison results to result . Here are the formulas for the different comparison methods one may use ( I denotes image , T template , R result ). The summation is done over template and/or the image patch: x' = 0...w-1, y' = 0...h-1

  • method=CV_TM_SQDIFF

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


    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=CV_TM_CCORR

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


    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=CV_TM_CCOEFF

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


    \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}


    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} }

After the function finishes the comparison, the best matches can be found as global minimums ( CV_TM_SQDIFF ) or maximums ( CV_TM_CCORR and CV_TM_CCOEFF ) using the MinMaxLoc function. In the case of a color image, template summation in the numerator and each sum in the denominator is done over all of the channels (and separate mean values are used for each channel).

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