potok/opencv
1
0
Fork 0
mirror of https://github.com/opencv/opencv.git synced 2026-07-30 15:53:03 +04:00
Code Packages Releases Wiki Activity

Tutorial Filter2D

Browse Source
This commit is contained in:
tribta
2017-08-24 16:22:27 +01:00
parent 08515281b9
commit a6f5e1f0ca
5 changed files with 295 additions and 85 deletions
Download Patch File Download Diff File Expand all files Collapse all files
doc/tutorials/imgproc/imgtrans/filter_2d/filter_2d.markdown
+110 -41
View File
@@ -1,12 +1,15 @@
Making your own linear filters! {#tutorial_filter_2d}
===============================
@prev_tutorial{tutorial_threshold_inRange}
@next_tutorial{tutorial_copyMakeBorder}
Goal
----
In this tutorial you will learn how to:
- Use the OpenCV function @ref cv::filter2D to create your own linear filters.
- Use the OpenCV function **filter2D()** to create your own linear filters.
Theory
------
@@ -40,61 +43,127 @@ Expressing the procedure above in the form of an equation we would have:
\f[H(x,y) = \sum_{i=0}^{M_{i} - 1} \sum_{j=0}^{M_{j}-1} I(x+i - a_{i}, y + j - a_{j})K(i,j)\f]
Fortunately, OpenCV provides you with the function @ref cv::filter2D so you do not have to code all
Fortunately, OpenCV provides you with the function **filter2D()** so you do not have to code all
these operations.
### What does this program do?
- Loads an image
- Performs a *normalized box filter*. For instance, for a kernel of size \f$size = 3\f$, the
kernel would be:
\f[K = \dfrac{1}{3 \cdot 3} \begin{bmatrix}
1 & 1 & 1 \\
1 & 1 & 1 \\
1 & 1 & 1
\end{bmatrix}\f]
The program will perform the filter operation with kernels of sizes 3, 5, 7, 9 and 11.
- The filter output (with each kernel) will be shown during 500 milliseconds
Code
----
-# **What does this program do?**
- Loads an image
- Performs a *normalized box filter*. For instance, for a kernel of size \f$size = 3\f$, the
kernel would be:
The tutorial code's is shown in the lines below.
\f[K = \dfrac{1}{3 \cdot 3} \begin{bmatrix}
1 & 1 & 1 \\
1 & 1 & 1 \\
1 & 1 & 1
\end{bmatrix}\f]
@add_toggle_cpp
You can also download it from
[here](https://raw.githubusercontent.com/opencv/opencv/master/samples/cpp/tutorial_code/ImgTrans/filter2D_demo.cpp)
@include cpp/tutorial_code/ImgTrans/filter2D_demo.cpp
@end_toggle
The program will perform the filter operation with kernels of sizes 3, 5, 7, 9 and 11.
@add_toggle_java
You can also download it from
[here](https://raw.githubusercontent.com/opencv/opencv/master/samples/java/tutorial_code/ImgTrans/Filter2D/Filter2D_Demo.java)
@include java/tutorial_code/ImgTrans/Filter2D/Filter2D_Demo.java
@end_toggle
- The filter output (with each kernel) will be shown during 500 milliseconds
-# The tutorial code's is shown lines below. You can also download it from
[here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/ImgTrans/filter2D_demo.cpp)
@include cpp/tutorial_code/ImgTrans/filter2D_demo.cpp
@add_toggle_python
You can also download it from
[here](https://raw.githubusercontent.com/opencv/opencv/master/samples/python/tutorial_code/ImgTrans/Filter2D/filter2D.py)
@include python/tutorial_code/ImgTrans/Filter2D/filter2D.py
@end_toggle
Explanation
-----------
-# Load an image
@snippet cpp/tutorial_code/ImgTrans/filter2D_demo.cpp load
-# Initialize the arguments for the linear filter
@snippet cpp/tutorial_code/ImgTrans/filter2D_demo.cpp init_arguments
-# Perform an infinite loop updating the kernel size and applying our linear filter to the input
image. Let's analyze that more in detail:
-# First we define the kernel our filter is going to use. Here it is:
@snippet cpp/tutorial_code/ImgTrans/filter2D_demo.cpp update_kernel
The first line is to update the *kernel_size* to odd values in the range: \f$[3,11]\f$. The second
line actually builds the kernel by setting its value to a matrix filled with \f$1's\f$ and
normalizing it by dividing it between the number of elements.
#### Load an image
-# After setting the kernel, we can generate the filter by using the function @ref cv::filter2D :
@snippet cpp/tutorial_code/ImgTrans/filter2D_demo.cpp apply_filter
The arguments denote:
@add_toggle_cpp
@snippet cpp/tutorial_code/ImgTrans/filter2D_demo.cpp load
@end_toggle
-# *src*: Source image
-# *dst*: Destination image
-# *ddepth*: The depth of *dst*. A negative value (such as \f$-1\f$) indicates that the depth is
@add_toggle_java
@snippet java/tutorial_code/ImgTrans/Filter2D/Filter2D_Demo.java load
@end_toggle
@add_toggle_python
@snippet python/tutorial_code/ImgTrans/Filter2D/filter2D.py load
@end_toggle
#### Initialize the arguments
@add_toggle_cpp
@snippet cpp/tutorial_code/ImgTrans/filter2D_demo.cpp init_arguments
@end_toggle
@add_toggle_java
@snippet java/tutorial_code/ImgTrans/Filter2D/Filter2D_Demo.java init_arguments
@end_toggle
@add_toggle_python
@snippet python/tutorial_code/ImgTrans/Filter2D/filter2D.py init_arguments
@end_toggle
##### Loop
Perform an infinite loop updating the kernel size and applying our linear filter to the input
image. Let's analyze that more in detail:
- First we define the kernel our filter is going to use. Here it is:
@add_toggle_cpp
@snippet cpp/tutorial_code/ImgTrans/filter2D_demo.cpp update_kernel
@end_toggle
@add_toggle_java
@snippet java/tutorial_code/ImgTrans/Filter2D/Filter2D_Demo.java update_kernel
@end_toggle
@add_toggle_python
@snippet python/tutorial_code/ImgTrans/Filter2D/filter2D.py update_kernel
@end_toggle
The first line is to update the *kernel_size* to odd values in the range: \f$[3,11]\f$.
The second line actually builds the kernel by setting its value to a matrix filled with
\f$1's\f$ and normalizing it by dividing it between the number of elements.
- After setting the kernel, we can generate the filter by using the function **filter2D()** :
@add_toggle_cpp
@snippet cpp/tutorial_code/ImgTrans/filter2D_demo.cpp apply_filter
@end_toggle
@add_toggle_java
@snippet java/tutorial_code/ImgTrans/Filter2D/Filter2D_Demo.java apply_filter
@end_toggle
@add_toggle_python
@snippet python/tutorial_code/ImgTrans/Filter2D/filter2D.py apply_filter
@end_toggle
- The arguments denote:
- *src*: Source image
- *dst*: Destination image
- *ddepth*: The depth of *dst*. A negative value (such as \f$-1\f$) indicates that the depth is
the same as the source.
-# *kernel*: The kernel to be scanned through the image
-# *anchor*: The position of the anchor relative to its kernel. The location *Point(-1, -1)*
indicates the center by default.
-# *delta*: A value to be added to each pixel during the correlation. By default it is \f$0\f$
-# *BORDER_DEFAULT*: We let this value by default (more details in the following tutorial)
- *kernel*: The kernel to be scanned through the image
- *anchor*: The position of the anchor relative to its kernel. The location *Point(-1, -1)*
indicates the center by default.
- *delta*: A value to be added to each pixel during the correlation. By default it is \f$0\f$
- *BORDER_DEFAULT*: We let this value by default (more details in the following tutorial)
-# Our program will effectuate a *while* loop, each 500 ms the kernel size of our filter will be
- Our program will effectuate a *while* loop, each 500 ms the kernel size of our filter will be
updated in the range indicated.
Results
@@ -104,4 +173,4 @@ Results
result should be a window that shows an image blurred by a normalized filter. Each 0.5 seconds
the kernel size should change, as can be seen in the series of snapshots below:
![](images/filter_2d_tutorial_result.jpg)
![](images/filter_2d_tutorial_result.jpg)
doc/tutorials/imgproc/table_of_content_imgproc.markdown
+2
View File
@@ -77,6 +77,8 @@ In this section you will learn about the image processing (manipulation) functio
- @subpage tutorial_filter_2d
*Languages:* C++, Java, Python
*Compatibility:* \> OpenCV 2.0
*Author:* Ana Huamán