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Merge pull request #3304 from eltermann:master

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Maksim Shabunin 2014-10-07 07:35:58 +00:00
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doc/py_tutorials/py_imgproc/py_histograms/py_histogram_begins/py_histogram_begins.rst
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@ -45,7 +45,7 @@ So now we use **cv2.calcHist()** function to find the histogram. Let's familiari
.. centered:: *cv2.calcHist(images, channels, mask, histSize, ranges[, hist[, accumulate]])* .. centered:: *cv2.calcHist(images, channels, mask, histSize, ranges[, hist[, accumulate]])*
#. images : it is the source image of type uint8 or float32. it should be given in square brackets, ie, "[img]". #. images : it is the source image of type uint8 or float32. it should be given in square brackets, ie, "[img]".
#. channels : it is also given in square brackets. It the index of channel for which we calculate histogram. For example, if input is grayscale image, its value is [0]. For color image, you can pass [0],[1] or [2] to calculate histogram of blue,green or red channel respectively. #. channels : it is also given in square brackets. It is the index of channel for which we calculate histogram. For example, if input is grayscale image, its value is [0]. For color image, you can pass [0], [1] or [2] to calculate histogram of blue, green or red channel respectively.
#. mask : mask image. To find histogram of full image, it is given as "None". But if you want to find histogram of particular region of image, you have to create a mask image for that and give it as mask. (I will show an example later.) #. mask : mask image. To find histogram of full image, it is given as "None". But if you want to find histogram of particular region of image, you have to create a mask image for that and give it as mask. (I will show an example later.)
#. histSize : this represents our BIN count. Need to be given in square brackets. For full scale, we pass [256]. #. histSize : this represents our BIN count. Need to be given in square brackets. For full scale, we pass [256].
#. ranges : this is our RANGE. Normally, it is [0,256]. #. ranges : this is our RANGE. Normally, it is [0,256].