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changing many instances of the same grammar error in documentation

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jeremy
2014-09-09 17:03:59 -05:00
parent f03b11d271
commit 56091bae1f
11 changed files with 11 additions and 11 deletions
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doc/py_tutorials/py_imgproc/py_histograms/py_2d_histogram/py_2d_histogram.rst
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@@ -64,7 +64,7 @@ The result we get is a two dimensional array of size 180x256. So we can show the
Method - 2 : Using Matplotlib
------------------------------
We can use **matplotlib.pyplot.imshow()** function to plot 2D histogram with different color maps. It gives us much more better idea about the different pixel density. But this also, doesn't gives us idea what color is there on a first look, unless you know the Hue values of different colors. Still I prefer this method. It is simple and better.
We can use **matplotlib.pyplot.imshow()** function to plot 2D histogram with different color maps. It gives us a much better idea about the different pixel density. But this also, doesn't gives us idea what color is there on a first look, unless you know the Hue values of different colors. Still I prefer this method. It is simple and better.
.. note:: While using this function, remember, interpolation flag should be ``nearest`` for better results.

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doc/py_tutorials/py_imgproc/py_histograms/py_histogram_backprojection/py_histogram_backprojection.rst
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@@ -15,7 +15,7 @@ It was proposed by **Michael J. Swain , Dana H. Ballard** in their paper **Index
**What is it actually in simple words?** It is used for image segmentation or finding objects of interest in an image. In simple words, it creates an image of the same size (but single channel) as that of our input image, where each pixel corresponds to the probability of that pixel belonging to our object. In more simpler worlds, the output image will have our object of interest in more white compared to remaining part. Well, that is an intuitive explanation. (I can't make it more simpler). Histogram Backprojection is used with camshift algorithm etc.
**How do we do it ?** We create a histogram of an image containing our object of interest (in our case, the ground, leaving player and other things). The object should fill the image as far as possible for better results. And a color histogram is preferred over grayscale histogram, because color of the object is more better way to define the object than its grayscale intensity. We then "back-project" this histogram over our test image where we need to find the object, ie in other words, we calculate the probability of every pixel belonging to the ground and show it. The resulting output on proper thresholding gives us the ground alone.
**How do we do it ?** We create a histogram of an image containing our object of interest (in our case, the ground, leaving player and other things). The object should fill the image as far as possible for better results. And a color histogram is preferred over grayscale histogram, because color of the object is a better way to define the object than its grayscale intensity. We then "back-project" this histogram over our test image where we need to find the object, ie in other words, we calculate the probability of every pixel belonging to the ground and show it. The resulting output on proper thresholding gives us the ground alone.
Algorithm in Numpy
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