* Apply two very common morphology operators: Dilation and Erosion. For this purpose, you will use the following OpenCV functions:
*:erode:`erode <>`
*:dilate:`dilate <>`
Cool Theory
============
..note::
The explanation below belongs to the book **Learning OpenCV** by Bradski and Kaehler.
Morphological Operations
--------------------------
* In short: A set of operations that process images based on shapes. Morphological operations apply a *structuring element* to an input image and generate an output image.
* The most basic morphological operations are two: Erosion and Dilation. They have a wide array of uses, i.e. :
* Removing noise
* Isolation of individual elements and joining disparate elements in an image.
* Finding of intensity bumps or holes in an image
* We will explain dilation and erosion briefly, using the following image as an example:
* This operations consists of convoluting an image :math:`A` with some kernel (:math:`B`), which can have any shape or size, usually a square or circle.
* The kernel :math:`B` has a defined *anchor point*, usually being the center of the kernel.
* As the kernel :math:`B` is scanned over the image, we compute the maximal pixel value overlapped by :math:`B` and replace the image pixel in the anchor point position with that maximal value. As you can deduce, this maximizing operation causes bright regions within an image to "grow" (therefore the name *dilation*). Take as an example the image above. Applying dilation we can get:
The background (bright) dilates around the black regions of the letter.
Erosion
^^^^^^^^
* This operation is the sister of dilation. What this does is to compute a local minimum over the area of the kernel.
* As the kernel :math:`B` is scanned over the image, we compute the minimal pixel value overlapped by :math:`B` and replace the image pixel under the anchor point with that minimal value.
* Analagously to the example for dilation, we can apply the erosion operator to the original image (shown above). You can see in the result below that the bright areas of the image (the background, apparently), get thinner, whereas the dark zones (the "writing"( gets bigger.
This tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/tutorial_code/ImgProc/Morphology_1.cpp>`_
Mat element = getStructuringElement( dilation_type,
Size( 2*dilation_size + 1, 2*dilation_size+1 ),
Point( dilation_size, dilation_size ) );
/// Apply the dilation operation
dilate( src, dilation_dst, element );
imshow( "Dilation Demo", dilation_dst );
}
Explanation
=============
#. Most of the stuff shown is known by you (if you have any doubt, please refer to the tutorials in previous sections). Let's check the general structure of the program:
* Every time we move any slider, the user's function **Erosion** or **Dilation** will be called and it will update the output image based on the current trackbar values.
Mat element = getStructuringElement( erosion_type,
Size( 2*erosion_size + 1, 2*erosion_size+1 ),
Point( erosion_size, erosion_size ) );
/// Apply the erosion operation
erode( src, erosion_dst, element );
imshow( "Erosion Demo", erosion_dst );
}
* The function that performs the *erosion* operation is :erode:`erode <>`. As we can see, it receives three arguments:
**src*: The source image
**erosion_dst*: The output image
**element*: This is the kernel we will use to perform the operation. If we do not specify, the default is a simple :math:`3x3` matrix. Otherwise, we can specify its shape. For this, we need to use the function :get_structuring_element:`getStructuringElement <>`:
..code-block:: cpp
Mat element = getStructuringElement( erosion_type,
* That is all. We are ready to perform the erosion of our image.
..note::
Additionally, there is another parameter that allows you to perform multiple erosions (iterations) at once. We are not using it in this simple tutorial, though. You can check out the Reference for more details.
#.**dilation:**
The code is below. As you can see, it is completely similar to the snippet of code for **erosion**. Here we also have the option of defining our kernel, its anchor point and the size of the operator to be used.
We get the results below. Varying the indices in the Trackbars give different output images, naturally. Try them out! You can even try to add a third Trackbar to control the number of iterations.
