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Merge pull request opencv#9424 from Cartucho:update_imgproc_tutorials
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‎doc/tutorials/imgproc/gausian_median_blur_bilateral_filter/gausian_median_blur_bilateral_filter.markdown

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Smoothing Images {#tutorial_gausian_median_blur_bilateral_filter}
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================
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@next_tutorial{tutorial_erosion_dilatation}
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Goal
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----
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In this tutorial you will learn how to apply diverse linear filters to smooth images using OpenCV
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functions such as:
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- @ref cv::blur
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- @ref cv::GaussianBlur
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- @ref cv::medianBlur
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- @ref cv::bilateralFilter
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- **blur()**
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- **GaussianBlur()**
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- **medianBlur()**
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- **bilateralFilter()**
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Theory
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------
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- Loads an image
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- Applies 4 different kinds of filters (explained in Theory) and show the filtered images
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sequentially
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@add_toggle_cpp
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- **Downloadable code**: Click
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[here](https://raw.githubusercontent.com/opencv/opencv/master/samples/cpp/tutorial_code/ImgProc/Smoothing/Smoothing.cpp)
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- **Code at glance:**
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@include samples/cpp/tutorial_code/ImgProc/Smoothing/Smoothing.cpp
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@end_toggle
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@add_toggle_java
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- **Downloadable code**: Click
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[here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/ImgProc/Smoothing.cpp)
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[here](https://raw.githubusercontent.com/opencv/opencv/master/samples/java/tutorial_code/ImgProc/Smoothing/Smoothing.java)
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- **Code at glance:**
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@include samples/java/tutorial_code/ImgProc/Smoothing/Smoothing.java
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@end_toggle
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@add_toggle_python
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- **Downloadable code**: Click
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[here](https://raw.githubusercontent.com/opencv/opencv/master/samples/python/tutorial_code/imgProc/Smoothing/smoothing.py)
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- **Code at glance:**
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@include samples/cpp/tutorial_code/ImgProc/Smoothing.cpp
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@include samples/python/tutorial_code/imgProc/Smoothing/smoothing.py
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@end_toggle
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Explanation
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-----------
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-# Let's check the OpenCV functions that involve only the smoothing procedure, since the rest is
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already known by now.
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-# **Normalized Block Filter:**
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Let's check the OpenCV functions that involve only the smoothing procedure, since the rest is
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already known by now.
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OpenCV offers the function @ref cv::blur to perform smoothing with this filter.
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@snippet cpp/tutorial_code/ImgProc/Smoothing.cpp blur
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#### Normalized Block Filter:
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- OpenCV offers the function **blur()** to perform smoothing with this filter.
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We specify 4 arguments (more details, check the Reference):
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- *src*: Source image
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- *dst*: Destination image
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- *Size( w,h )*: Defines the size of the kernel to be used ( of width *w* pixels and height
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- *Size( w, h )*: Defines the size of the kernel to be used ( of width *w* pixels and height
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*h* pixels)
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- *Point(-1, -1)*: Indicates where the anchor point (the pixel evaluated) is located with
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respect to the neighborhood. If there is a negative value, then the center of the kernel is
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considered the anchor point.
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-# **Gaussian Filter:**
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@add_toggle_cpp
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@snippet cpp/tutorial_code/ImgProc/Smoothing/Smoothing.cpp blur
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@end_toggle
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It is performed by the function @ref cv::GaussianBlur :
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@snippet cpp/tutorial_code/ImgProc/Smoothing.cpp gaussianblur
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@add_toggle_java
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@snippet samples/java/tutorial_code/ImgProc/Smoothing/Smoothing.java blur
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@end_toggle
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Here we use 4 arguments (more details, check the OpenCV reference):
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@add_toggle_python
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@snippet samples/python/tutorial_code/imgProc/Smoothing/smoothing.py blur
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@end_toggle
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#### Gaussian Filter:
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- It is performed by the function **GaussianBlur()** :
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Here we use 4 arguments (more details, check the OpenCV reference):
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- *src*: Source image
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- *dst*: Destination image
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- *Size(w, h)*: The size of the kernel to be used (the neighbors to be considered). \f$w\f$ and
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- \f$\sigma_{y}\f$: The standard deviation in y. Writing \f$0\f$ implies that \f$\sigma_{y}\f$ is
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calculated using kernel size.
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-# **Median Filter:**
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@add_toggle_cpp
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@snippet cpp/tutorial_code/ImgProc/Smoothing/Smoothing.cpp gaussianblur
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@end_toggle
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This filter is provided by the @ref cv::medianBlur function:
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@snippet cpp/tutorial_code/ImgProc/Smoothing.cpp medianblur
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@add_toggle_java
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@snippet samples/java/tutorial_code/ImgProc/Smoothing/Smoothing.java gaussianblur
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@end_toggle
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We use three arguments:
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@add_toggle_python
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@snippet samples/python/tutorial_code/imgProc/Smoothing/smoothing.py gaussianblur
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@end_toggle
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#### Median Filter:
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- This filter is provided by the **medianBlur()** function:
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We use three arguments:
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- *src*: Source image
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- *dst*: Destination image, must be the same type as *src*
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- *i*: Size of the kernel (only one because we use a square window). Must be odd.
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-# **Bilateral Filter**
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@add_toggle_cpp
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@snippet cpp/tutorial_code/ImgProc/Smoothing/Smoothing.cpp medianblur
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@end_toggle
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Provided by OpenCV function @ref cv::bilateralFilter
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@snippet cpp/tutorial_code/ImgProc/Smoothing.cpp bilateralfilter
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@add_toggle_java
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@snippet samples/java/tutorial_code/ImgProc/Smoothing/Smoothing.java medianblur
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@end_toggle
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We use 5 arguments:
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@add_toggle_python
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@snippet samples/python/tutorial_code/imgProc/Smoothing/smoothing.py medianblur
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@end_toggle
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#### Bilateral Filter
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- Provided by OpenCV function **bilateralFilter()**
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We use 5 arguments:
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- *src*: Source image
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- *dst*: Destination image
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- *d*: The diameter of each pixel neighborhood.
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- \f$\sigma_{Color}\f$: Standard deviation in the color space.
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- \f$\sigma_{Space}\f$: Standard deviation in the coordinate space (in pixel terms)
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@add_toggle_cpp
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@snippet cpp/tutorial_code/ImgProc/Smoothing/Smoothing.cpp bilateralfilter
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@end_toggle
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@add_toggle_java
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@snippet samples/java/tutorial_code/ImgProc/Smoothing/Smoothing.java bilateralfilter
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@end_toggle
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@add_toggle_python
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@snippet samples/python/tutorial_code/imgProc/Smoothing/smoothing.py bilateralfilter
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@end_toggle
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Results
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-------
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- The code opens an image (in this case *lena.jpg*) and display it under the effects of the 4
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filters explained.
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- The code opens an image (in this case [lena.jpg](https://raw.githubusercontent.com/opencv/opencv/master/samples/data/lena.jpg))
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and display it under the effects of the 4 filters explained.
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- Here is a snapshot of the image smoothed using *medianBlur*:
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![](images/Smoothing_Tutorial_Result_Median_Filter.jpg)

‎doc/tutorials/imgproc/hitOrMiss/hitOrMiss.markdown

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Hit-or-Miss {#tutorial_hitOrMiss}
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=================================
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@prev_tutorial{tutorial_opening_closing_hats}
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@next_tutorial{tutorial_morph_lines_detection}
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Goal
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----
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In this tutorial you will learn how to find a given configuration or pattern in a binary image by using the Hit-or-Miss transform (also known as Hit-and-Miss transform).
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This transform is also the basis of more advanced morphological operations such as thinning or pruning.
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We will use the OpenCV function @ref cv::morphologyEx.
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We will use the OpenCV function **morphologyEx()** .
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Hit-or-Miss theory
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-------------------
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Morphological operators process images based on their shape. These operators apply one or more *structuring elements* to an input image to obtain the output image.
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The two basic morphological operations are the *erosion* and the *dilation*. The combination of these two operations generate advanced morphological transformations such as *opening*, *closing*, or *top-hat* transform.
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To know more about these and other basic morphological operations refer to previous tutorials @ref tutorial_erosion_dilatation "here" and @ref tutorial_opening_closing_hats "here".
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To know more about these and other basic morphological operations refer to previous tutorials (@ref tutorial_erosion_dilatation "Eroding and Dilating") and (@ref tutorial_opening_closing_hats "More Morphology Transformations").
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The Hit-or-Miss transformation is useful to find patterns in binary images. In particular, it finds those pixels whose neighbourhood matches the shape of a first structuring element \f$B_1\f$
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while not matching the shape of a second structuring element \f$B_2\f$ at the same time. Mathematically, the operation applied to an image \f$A\f$ can be expressed as follows:
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Code
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----
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The code corresponding to the previous example is shown below. You can also download it from
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[here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/ImgProc/HitMiss.cpp)
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@include samples/cpp/tutorial_code/ImgProc/HitMiss.cpp
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The code corresponding to the previous example is shown below.
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@add_toggle_cpp
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You can also download it from
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[here](https://raw.githubusercontent.com/opencv/opencv/master/samples/cpp/tutorial_code/ImgProc/HitMiss/HitMiss.cpp)
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@include samples/cpp/tutorial_code/ImgProc/HitMiss/HitMiss.cpp
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@end_toggle
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@add_toggle_java
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You can also download it from
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[here](https://raw.githubusercontent.com/opencv/opencv/master/samples/java/tutorial_code/ImgProc/HitMiss/HitMiss.java)
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@include samples/java/tutorial_code/ImgProc/HitMiss/HitMiss.java
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@end_toggle
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@add_toggle_python
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You can also download it from
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[here](https://raw.githubusercontent.com/opencv/opencv/master/samples/python/tutorial_code/imgProc/HitMiss/hit_miss.py)
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@include samples/python/tutorial_code/imgProc/HitMiss/hit_miss.py
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@end_toggle
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As you can see, it is as simple as using the function @ref cv::morphologyEx with the operation type @ref cv::MORPH_HITMISS and the chosen kernel.
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As you can see, it is as simple as using the function **morphologyEx()** with the operation type **MORPH_HITMISS** and the chosen kernel.
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Other examples
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--------------

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