From ae649e8c30b8528c1d308e9adb2a5dc8bc820e05 Mon Sep 17 00:00:00 2001 From: Ana Huaman Date: Mon, 27 Jun 2011 04:01:01 +0000 Subject: [PATCH] Added rst Tutorial for Filter 2D --- .../imgproc/imgtrans/filter_2d/filter_2d.rst | 200 +++++++++++++++++++++ .../images/filter_2d_tutorial_kernel_theory.png | Bin 0 -> 10594 bytes .../filter_2d/images/filter_2d_tutorial_result.png | Bin 0 -> 127590 bytes .../images/imgtrans/Filter_2D_Tutorial_Cover.jpg | Bin 0 -> 30346 bytes .../table_of_content_imgproc.rst | 23 +++ 5 files changed, 223 insertions(+) create mode 100644 doc/tutorials/imgproc/imgtrans/filter_2d/filter_2d.rst create mode 100644 doc/tutorials/imgproc/imgtrans/filter_2d/images/filter_2d_tutorial_kernel_theory.png create mode 100644 doc/tutorials/imgproc/imgtrans/filter_2d/images/filter_2d_tutorial_result.png create mode 100644 doc/tutorials/imgproc/table_of_content_imgproc/images/imgtrans/Filter_2D_Tutorial_Cover.jpg diff --git a/doc/tutorials/imgproc/imgtrans/filter_2d/filter_2d.rst b/doc/tutorials/imgproc/imgtrans/filter_2d/filter_2d.rst new file mode 100644 index 0000000..aa5a69a --- /dev/null +++ b/doc/tutorials/imgproc/imgtrans/filter_2d/filter_2d.rst @@ -0,0 +1,200 @@ +.. _filter_2d: + +Making your own linear filters! +******************************** + +Goal +===== + +In this tutorial you will learn how to: + +* Use the OpenCV function :filter2d:`filter2D <>` to create your own linear filters. + +Theory +============ + +.. note:: + The explanation below belongs to the book **Learning OpenCV** by Bradski and Kaehler. + + +Convolution +------------ +In a very general sense, convolution is an operation between every part of an image and an operator (kernel). + +What is a kernel? +------------------ +A kernel is essentially a fixed size array of numerical coefficeints along with an *anchor point* in that array, which is tipically located at the center. + +.. image:: images/filter_2d_tutorial_kernel_theory.png + :alt: kernel example + :align: center + +How does convolution with a kernel work? +----------------------------------------- + +Assume you want to know the resulting value of a particular location in the image. The value of the convolution is calculated in the following way: + +#. Place the kernel anchor on top of a determined pixel, with the rest of the kernel overlaying the corresponding local pixels in the image. + +#. Multiply the kernel coefficients by the corresponding image pixel values and sum the result. + +#. Place the result to the location of the *anchor* in the input image. + +#. Repeat the process for all pixels by scanning the kernel over the entire image. + +Expressing the procedure above in the form of an equation we would have: + +.. math:: + + 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) + +Fortunately, OpenCV provides you with the function :filter2d:`filter2D <>` so you do not have to code all these operations. + +Code +====== + +#. **What does this program do?** + + * Loads an image + * Performs a *normalized box filter*. For instance, for a kernel of size :math:`size = 3`, the kernel would be: + + .. math:: + + K = \dfrac{1}{3 \cdot 3} \begin{bmatrix} + 1 & 1 & 1 \\ + 1 & 1 & 1 \\ + 1 & 1 & 1 + \end{bmatrix} + + 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 + +#. The tutorial code's is shown lines below. You can also download it from `here `_ + + +.. code-block:: cpp + + #include "opencv2/imgproc/imgproc.hpp" + #include "opencv2/highgui/highgui.hpp" + #include + #include + + using namespace cv; + + /** @function main */ + int main ( int argc, char** argv ) + { + /// Declare variables + Mat src, dst; + + Mat kernel; + Point anchor; + double delta; + int ddepth; + int kernel_size; + char* window_name = "filter2D Demo"; + + int c; + + /// Load an image + src = imread( argv[1] ); + + if( !src.data ) + { return -1; } + + /// Create window + namedWindow( window_name, CV_WINDOW_AUTOSIZE ); + + /// Initialize arguments for the filter + anchor = Point( -1, -1 ); + delta = 0; + ddepth = -1; + + /// Loop - Will filter the image with different kernel sizes each 0.5 seconds + int ind = 0; + while( true ) + { + c = waitKey(500); + /// Press 'ESC' to exit the program + if( (char)c == 27 ) + { break; } + + /// Update kernel size for a normalized box filter + kernel_size = 3 + 2*( ind%5 ); + kernel = Mat::ones( kernel_size, kernel_size, CV_32F )/ (float)(kernel_size*kernel_size); + + /// Apply filter + filter2D(src, dst, ddepth , kernel, anchor, delta, BORDER_DEFAULT ); + imshow( window_name, dst ); + ind++; + } + + return 0; + } +Explanation +============= + +#. We begin with the usual steps: + + * Load an image + + .. code-block:: cpp + + src = imread( argv[1] ); + + if( !src.data ) + { return -1; } + + * Create a window to display the result + + .. code-block:: cpp + + namedWindow( window_name, CV_WINDOW_AUTOSIZE ); + +#. Initialize the arguments for the linear filter + + .. code-block:: cpp + + anchor = Point( -1, -1 ); + delta = 0; + ddepth = -1; + + +#. 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: + + .. code-block:: cpp + + kernel_size = 3 + 2*( ind%5 ); + kernel = Mat::ones( kernel_size, kernel_size, CV_32F )/ (float)(kernel_size*kernel_size); + + The first line is to update the *kernel_size* to odd values in the range: :math:`[3,11]`. The second line actually builds the kernel by setting its value to a matrix filled with :math:`1's` 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:`filter2D <>`: + + .. code-block:: cpp + + filter2D(src, dst, ddepth , kernel, anchor, delta, BORDER_DEFAULT ); + + The arguments denote: + + a. *src*: Source image + #. *dst*: Destination image + #. *ddepth*: The depth of *dst*. A negative value (such as :math:`-1`) 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 convolution. By default it is :math:`0` + #. *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 updated in the range indicated. + +Results +======== + +#. After compiling the code above, you can execute it giving as argument the path of an image. The 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: + + .. image:: images/filter_2d_tutorial_result.png + :alt: kernel example + :align: center diff --git a/doc/tutorials/imgproc/imgtrans/filter_2d/images/filter_2d_tutorial_kernel_theory.png b/doc/tutorials/imgproc/imgtrans/filter_2d/images/filter_2d_tutorial_kernel_theory.png new file mode 100644 index 0000000000000000000000000000000000000000..70d4e2d210584506d530b307b9dfd335eff56bcc GIT binary patch literal 10594 zcmbVybyQUC+V=oMBP}5v0uK(IDk*Id(nvFiG=ijb3JQqQN(?DTgM!i_4IC_d*HZEr-XZtYHuFZIC>fB00G0KW1>Pj} z(YdBU`k91{kdQtkq;L%0V)fB5^-=S1bHlp(pw#YQZGEr~+c&juRq8eQ}pV zsnAB{(G-@I=^34_#i;2sDq6+kRP>+ZB_;W>H`^EXh)m$wKR*4)UcPP>oRRjPC;>;7 zhP4%EDP49cOs>V0ThkWcOZx}JuYNG`G^c8|pXZR8n=5=SMB$&~v3>q#!pDa6<`n+$ zdHKdE_l$ubc~u&$G2z@~;@JbG<>is1&e8ljdW^)H1&`*og6^57MdO`D;HnSix)w!m zyUPgb%UamzGwx)yFZnFX2z7}kW6v+v)%sn#-C(Y7X?g0+)OFU0b3`uKw-;p?X?(24Se)yR-F4QHmWXkaEv}9aaq~EkA!r7!vwJuPKZ}nm||h@ 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