It can be found in the samples folder in OpenCV (**Drawing_1.cpp**)
* This code is in your OpenCV sample folder. Otherwise you can grab it from `here <https://code.ros.org/svn/opencv/trunk/opencv/samples/cpp/tutorial_code/Basic/Drawing_1.cpp>`_
* In this tutorial, we intend to use *random* values for the drawing parameters. Also, we intend to populate our image with a big number of geometric figures. Since we will be initializing them in a random fashion, this process will be automatic and made by using *loops*
* You will find the code in the *samples/cpp* folder of your OpenCV distribution. The file is **drawing_2.cpp**
* This code is in your OpenCV sample folder. Otherwise you can grab it from `here <https://code.ros.org/svn/opencv/trunk/opencv/samples/cpp/tutorial_code/Basic/Drawing_2.cpp>`_
* Apply diverse linear filters to smooth images using OpenCV functions such as:
* :blur:`blur <>`
* :gaussian_blur:`GaussianBlur <>`
* :median_blur:`medianBlur <>`
* :bilateral_filter:`bilateralFilter <>`
Cool Theory
============
.. note::
The explanation below belongs to the book `Computer Vision: Algorithms and Applications <http://szeliski.org/Book/>`_ by Richard Szeliski and to *LearningOpenCV*
* *Smoothing*, also called *blurring*, is a simple and frequently used image processing operation.
* There are many reasons for smoothing. In this tutorial we will focus on smoothing in order to reduce noise (other uses will be seen in the following tutorials).
* To perform a smoothing operation we will apply a *filter* to our image. The most common type of filters are *linear*, in which an output pixel's value (i.e. :math:`g(i,j)`) is determined as a weighted sum of input pixel values (i.e. :math:`f(i+k,j+l)`) :
.. math::
g(i,j) = \sum_{k,l} f(i+k, j+l) h(k,l)
:math:`h(k,l)` is called the *kernel*, which is nothing more than the coefficients of the filter.
It helps to visualize a *filter* as a window of coefficients sliding across the image.
* There are many kind of filters, here we will mention the most used:
Normalized Box Filter
-----------------------
* This filter is the simplest of all! Each output pixel is the *mean* of its kernel neighbors ( all of them contribute with equal weights)
* Just in case, the kernel is below:
.. math::
K = \dfrac{1}{K_{width} \cdot K_{height}} \begin{bmatrix}
1 & 1 & 1 & ... & 1 \\
1 & 1 & 1 & ... & 1 \\
. & . & . & ... & 1 \\
. & . & . & ... & 1 \\
1 & 1 & 1 & ... & 1
\end{bmatrix}
Gaussian Filter
---------------
* Probably the most useful filter (although not the fastest). Gaussian filtering is done by convolving each point in the input array with a *Gaussian kernel* and then summing them all to produce the output array.
* Just to make the picture clearer, remember how a 1D Gaussian kernel look like?
Assuming that an image is 1D, you can notice that the pixel located in the middle would have the biggest weight. The weight of its neighbors decreases as the spatial distance between them and the center pixel increases.
.. note::
* Remember that a 2D Gaussian can be represented as :
where :math:`\mu` is the mean (the peak) and :math:`\sigma` represents the variance (per each of the variables :math:`x` and :math:`y`)
Median Filter
--------------
The median filter run through each element of the signal (in this case the image) and replace each pixel with the **median** of its neighboring pixels (located in a square neighborhood around the evaluated pixel).
Bilateral Filter
-----------------
* So far, we have explained some filters which main goal is to *smooth* an input image. However, sometimes the filters do not only dissolve the noise, but also smooth away the *edges*. To avoid this (at certain extent at least), we can use a bilateral filter.
* In an analogous way as the Gaussian filter, the bilateral filter also considers the neighboring pixels with weights assigned to each of them. These weights have two components, the first of which is the same weighting used by the Gaussian filter. The second component takes into account the difference in intensity between the neighboring pixels and the evaluated one.
* For a more detailed explanation you can check `this link <http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/MANDUCHI1/Bilateral_Filtering.html>`_
Code
======
This tutorial code's is shown lines below. You can also download it from `here <https://code.ros.org/svn/opencv/trunk/opencv/samples/cpp/tutorial_code/Image_Processing/FilterDemo1.cpp>`_
We specify 4 arguments (more details, check the Reference):
* *src*: Source image
* *dst*: Destination image
* *Size( w,h )*: Defines the size of the kernel to be used ( of width *w* pixels and height *h* pixels)
* *Point(-1, -1)*: Indicates where the anchor point (the pixel evaluated) is located with respect to the neighborhood. If there is a negative value, then the center of the kernel is considered the anchor point.
#. **Gaussian Filter:**
It is performed by the function :gaussian_blur:`GaussianBlur <>` :
.. code-block:: cpp
for ( int i = 1; i < MAX_KERNEL_LENGTH; i = i + 2 )
Here we use 4 arguments (more details, check the OpenCV reference):
* *src*: Source image
* *dst*: Destination image
* *Size(w, h)*: The size of the kernel to be used (the neighbors to be considered). :math:`w` and :math:`h` have to be odd and positive numbers otherwise thi size will be calculated using the :math:`\sigma_{x}` and :math:`\sigma_{y}` arguments.
* :math:`\sigma_{x}`: The standard deviation in x. Writing :math:`0` implies that :math:`\sigma_{x}` is calculated using kernel size.
* :math:`\sigma_{y}`: The standard deviation in y. Writing :math:`0` implies that :math:`\sigma_{y}` is calculated using kernel size.
#. **Median Filter:**
This filter is provided by the :median_blur:`medianBlur <>` function:
.. code-block:: cpp
for ( int i = 1; i < MAX_KERNEL_LENGTH; i = i + 2 )
The following links describe a set of basic OpenCV tutorials. All the source code mentioned here is provide as part of the OpenCV regular releases, so check before you start copy & pasting the code. The list of tutorials below is automatically generated from reST files located in our SVN repository.
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