moved the new docs from docroot to doc

doc/opencv1/py/imgproc_object_detection.rst

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+Object Detection
+================
+
+.. highlight:: python
+
+
+
+.. index:: MatchTemplate
+
+.. _MatchTemplate:
+
+MatchTemplate
+-------------
+
+`id=0.180820664163 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/py/imgproc/MatchTemplate>`__
+
+
+.. function:: MatchTemplate(image,templ,result,method)-> None
+
+    Compares a template against overlapped image regions.
+
+
+
+
+
+    
+    :param image: Image where the search is running; should be 8-bit or 32-bit floating-point 
+    
+    :type image: :class:`CvArr`
+    
+    
+    :param templ: Searched template; must be not greater than the source image and the same data type as the image 
+    
+    :type templ: :class:`CvArr`
+    
+    
+    :param result: A map of comparison results; single-channel 32-bit floating-point.
+        If  ``image``  is  :math:`W \times H`  and ``templ``  is  :math:`w \times h`  then  ``result``  must be  :math:`(W-w+1) \times (H-h+1)` 
+    
+    :type result: :class:`CvArr`
+    
+    
+    :param method: Specifies the way the template must be compared with the image regions (see below) 
+    
+    :type method: int
+    
+    
+    
+The function is similar to
+:ref:`CalcBackProjectPatch`
+. It slides through 
+``image``
+, compares the
+overlapped patches of size 
+:math:`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 (
+:math:`I`
+denotes 
+``image``
+, 
+:math:`T`
+``template``
+,
+:math:`R`
+``result``
+). The summation is done over template and/or the
+image patch: 
+:math:`x' = 0...w-1, y' = 0...h-1`
+
+
+    
+
+* method=CV\_TM\_SQDIFF
+    
+    
+    .. math::
+    
+        R(x,y)= \sum _{x',y'} (T(x',y')-I(x+x',y+y'))^2  
+    
+    
+    
+
+* method=CV\_TM\_SQDIFF\_NORMED
+    
+    
+    .. math::
+    
+        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
+    
+    
+    .. math::
+    
+        R(x,y)= \sum _{x',y'} (T(x',y')  \cdot I(x+x',y+y'))  
+    
+    
+    
+
+* method=CV\_TM\_CCORR\_NORMED
+    
+    
+    .. math::
+    
+        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
+    
+    
+    .. math::
+    
+        R(x,y)= \sum _{x',y'} (T'(x',y')  \cdot I(x+x',y+y'))  
+    
+    
+    where
+    
+    
+    .. math::
+    
+        \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=CV\_TM\_CCOEFF\_NORMED
+    
+    
+    .. math::
+    
+        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 
+:ref:`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).
+