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Updated sample files documentation inclusions

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This commit is contained in:
Maksim Shabunin
2014-12-26 14:35:46 +03:00
parent ec9a17e71a
commit b4050c775e
28 changed files with 171 additions and 183 deletions
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13
doc/tutorials/core/discrete_fourier_transform/discrete_fourier_transform.markdown
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@@ -15,21 +15,14 @@ Source code
-----------
You can [download this from here
](samples/cpp/tutorial_code/core/discrete_fourier_transform/discrete_fourier_transform.cpp) or
](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/core/discrete_fourier_transform/discrete_fourier_transform.cpp) or
find it in the
`samples/cpp/tutorial_code/core/discrete_fourier_transform/discrete_fourier_transform.cpp` of the
OpenCV source code library.
Here's a sample usage of @ref cv::dft() :
@dontinclude cpp/tutorial_code/core/discrete_fourier_transform/discrete_fourier_transform.cpp
@until highgui.hpp
@skipline iostream
@skip main
@until {
@skip filename
@until return 0;
@until }
@includelineno cpp/tutorial_code/core/discrete_fourier_transform/discrete_fourier_transform.cpp
Explanation
-----------
@@ -147,7 +140,7 @@ An application idea would be to determine the geometrical orientation present in
example, let us find out if a text is horizontal or not? Looking at some text you'll notice that the
text lines sort of form also horizontal lines and the letters form sort of vertical lines. These two
main components of a text snippet may be also seen in case of the Fourier transform. Let us use
[this horizontal ](samples/data/imageTextN.png) and [this rotated](samples/data/imageTextR.png)
[this horizontal ](https://github.com/Itseez/opencv/tree/master/samples/data/imageTextN.png) and [this rotated](https://github.com/Itseez/opencv/tree/master/samples/data/imageTextR.png)
image about a text.
In case of the horizontal text:

9
doc/tutorials/core/file_input_output_with_xml_yml/file_input_output_with_xml_yml.markdown
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@@ -16,18 +16,13 @@ Source code
-----------
You can [download this from here
](samples/cpp/tutorial_code/core/file_input_output/file_input_output.cpp) or find it in the
](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/core/file_input_output/file_input_output.cpp) or find it in the
`samples/cpp/tutorial_code/core/file_input_output/file_input_output.cpp` of the OpenCV source code
library.
Here's a sample code of how to achieve all the stuff enumerated at the goal list.
@dontinclude cpp/tutorial_code/core/file_input_output/file_input_output.cpp
@until std;
@skip class MyData
@until return 0;
@until }
@includelineno cpp/tutorial_code/core/file_input_output/file_input_output.cpp
Explanation
-----------

26
doc/tutorials/core/how_to_scan_images/how_to_scan_images.markdown
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@@ -51,7 +51,7 @@ three major ways of going through an image pixel by pixel. To make things a litt
will make the scanning for each image using all of these methods, and print out how long it took.
You can download the full source code [here
](samples/cpp/tutorial_code/core/how_to_scan_images/how_to_scan_images.cpp) or look it up in
](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/core/how_to_scan_images/how_to_scan_images.cpp) or look it up in
the samples directory of OpenCV at the cpp tutorial code for the core section. Its basic usage is:
@code{.bash}
how_to_scan_images imageName.jpg intValueToReduce [G]
@@ -59,10 +59,7 @@ how_to_scan_images imageName.jpg intValueToReduce [G]
The final argument is optional. If given the image will be loaded in gray scale format, otherwise
the RGB color way is used. The first thing is to calculate the lookup table.
@dontinclude cpp/tutorial_code/core/how_to_scan_images/how_to_scan_images.cpp
@skip int divideWith
@until table[i]
@snippet how_to_scan_images.cpp dividewith
Here we first use the C++ *stringstream* class to convert the third command line argument from text
to an integer format. Then we use a simple look and the upper formula to calculate the lookup table.
@@ -107,9 +104,7 @@ The efficient way
When it comes to performance you cannot beat the classic C style operator[] (pointer) access.
Therefore, the most efficient method we can recommend for making the assignment is:
@skip Mat& ScanImageAndReduceC
@until return
@until }
@snippet how_to_scan_images.cpp scan-c
Here we basically just acquire a pointer to the start of each row and go through it until it ends.
In the special case that the matrix is stored in a continues manner we only need to request the
@@ -141,9 +136,7 @@ considered a safer way as it takes over these tasks from the user. All you need
begin and the end of the image matrix and then just increase the begin iterator until you reach the
end. To acquire the value *pointed* by the iterator use the \* operator (add it before it).
@skip ScanImageAndReduceIterator
@until return
@until }
@snippet how_to_scan_images.cpp scan-iterator
In case of color images we have three uchar items per column. This may be considered a short vector
of uchar items, that has been baptized in OpenCV with the *Vec3b* name. To access the n-th sub
@@ -161,9 +154,7 @@ what type we are looking at the image. It's no different here as you need manual
type to use at the automatic lookup. You can observe this in case of the gray scale images for the
following source code (the usage of the + @ref cv::at() function):
@skip ScanImageAndReduceRandomAccess
@until return
@until }
@snippet how_to_scan_images.cpp scan-random
The functions takes your input type and coordinates and calculates on the fly the address of the
queried item. Then returns a reference to that. This may be a constant when you *get* the value and
@@ -192,14 +183,11 @@ OpenCV has a function that makes the modification without the need from you to w
the image. We use the @ref cv::LUT() function of the core module. First we build a Mat type of the
lookup table:
@dontinclude cpp/tutorial_code/core/how_to_scan_images/how_to_scan_images.cpp
@skip Mat lookUpTable
@until p[i] = table[i]
@snippet how_to_scan_images.cpp table-init
Finally call the function (I is our input image and J the output one):
@skipline LUT
@snippet how_to_scan_images.cpp table-use
Performance Difference
----------------------

2
doc/tutorials/core/how_to_use_ippa_conversion/how_to_use_ippa_conversion.markdown
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@@ -16,7 +16,7 @@ Code
You may also find the source code in the
`samples/cpp/tutorial_code/core/ippasync/ippasync_sample.cpp` file of the OpenCV source library or
download it from [here](samples/cpp/tutorial_code/core/ippasync/ippasync_sample.cpp).
download it from [here](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/core/ippasync/ippasync_sample.cpp).
@includelineno cpp/tutorial_code/core/ippasync/ippasync_sample.cpp

24
doc/tutorials/core/interoperability_with_OpenCV_1/interoperability_with_OpenCV_1.markdown
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@@ -85,7 +85,7 @@ L = Mat(pI);
A case study
------------
Now that you have the basics done [here's](samples/cpp/tutorial_code/core/interoperability_with_OpenCV_1/interoperability_with_OpenCV_1.cpp)
Now that you have the basics done [here's](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/core/interoperability_with_OpenCV_1/interoperability_with_OpenCV_1.cpp)
an example that mixes the usage of the C interface with the C++ one. You will also find it in the
sample directory of the OpenCV source code library at the
`samples/cpp/tutorial_code/core/interoperability_with_OpenCV_1/interoperability_with_OpenCV_1.cpp` .
@@ -93,18 +93,13 @@ To further help on seeing the difference the programs supports two modes: one mi
one pure C++. If you define the *DEMO_MIXED_API_USE* you'll end up using the first. The program
separates the color planes, does some modifications on them and in the end merge them back together.
@dontinclude cpp/tutorial_code/core/interoperability_with_OpenCV_1/interoperability_with_OpenCV_1.cpp
@until namespace cv
@skip ifdef
@until endif
@skip main
@until endif
@snippet interoperability_with_OpenCV_1.cpp head
@snippet interoperability_with_OpenCV_1.cpp start
Here you can observe that with the new structure we have no pointer problems, although it is
possible to use the old functions and in the end just transform the result to a *Mat* object.
@skip convert image
@until split
@snippet interoperability_with_OpenCV_1.cpp new
Because, we want to mess around with the images luma component we first convert from the default RGB
to the YUV color space and then split the result up into separate planes. Here the program splits:
@@ -114,8 +109,7 @@ image some Gaussian noise and then mix together the channels according to some f
The scanning version looks like:
@skip #if 1
@until #else
@snippet interoperability_with_OpenCV_1.cpp scanning
Here you can observe that we may go through all the pixels of an image in three fashions: an
iterator, a C pointer and an individual element access style. You can read a more in-depth
@@ -123,14 +117,12 @@ description of these in the @ref tutorial_how_to_scan_images tutorial. Convertin
names is easy. Just remove the cv prefix and use the new *Mat* data structure. Here's an example of
this by using the weighted addition function:
@until planes[0]
@until endif
@snippet interoperability_with_OpenCV_1.cpp noisy
As you may observe the *planes* variable is of type *Mat*. However, converting from *Mat* to
*IplImage* is easy and made automatically with a simple assignment operator.
@skip merge(planes
@until #endif
@snippet interoperability_with_OpenCV_1.cpp end
The new *imshow* highgui function accepts both the *Mat* and *IplImage* data structures. Compile and
run the program and if the first image below is your input you may get either the first or second as
@@ -140,7 +132,7 @@ output:
You may observe a runtime instance of this on the [YouTube
here](https://www.youtube.com/watch?v=qckm-zvo31w) and you can [download the source code from here
](samples/cpp/tutorial_code/core/interoperability_with_OpenCV_1/interoperability_with_OpenCV_1.cpp)
](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/core/interoperability_with_OpenCV_1/interoperability_with_OpenCV_1.cpp)
or find it in the
`samples/cpp/tutorial_code/core/interoperability_with_OpenCV_1/interoperability_with_OpenCV_1.cpp`
of the OpenCV source code library.

2
doc/tutorials/core/mat-mask-operations/mat_mask_operations.markdown
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@@ -133,7 +133,7 @@ For example:
![](images/resultMatMaskFilter2D.png)
You can download this source code from [here
](samples/cpp/tutorial_code/core/mat_mask_operations/mat_mask_operations.cpp) or look in the
](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/core/mat_mask_operations/mat_mask_operations.cpp) or look in the
OpenCV source code libraries sample directory at
`samples/cpp/tutorial_code/core/mat_mask_operations/mat_mask_operations.cpp`.

54
doc/tutorials/core/mat_the_basic_image_container/mat_the_basic_image_container.markdown
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@@ -144,16 +144,13 @@ file by using the @ref cv::imwrite() function. However, for debugging purposes i
convenient to see the actual values. You can do this using the \<\< operator of *Mat*. Be aware that
this only works for two dimensional matrices.
@dontinclude cpp/tutorial_code/core/mat_the_basic_image_container/mat_the_basic_image_container.cpp
Although *Mat* works really well as an image container, it is also a general matrix class.
Therefore, it is possible to create and manipulate multidimensional matrices. You can create a Mat
object in multiple ways:
- @ref cv::Mat::Mat Constructor
@skip Mat M(2
@until cout
@snippet mat_the_basic_image_container.cpp constructor
![](images/MatBasicContainerOut1.png)
@@ -162,7 +159,7 @@ object in multiple ways:
Then we need to specify the data type to use for storing the elements and the number of channels
per matrix point. To do this we have multiple definitions constructed according to the following
convention:
@code{.cpp}
@code
CV_[The number of bits per item][Signed or Unsigned][Type Prefix]C[The channel number]
@endcode
For instance, *CV_8UC3* means we use unsigned char types that are 8 bit long and each pixel has
@@ -173,18 +170,15 @@ object in multiple ways:
- Use C/C++ arrays and initialize via constructor
@skip int sz
@until Mat L
@snippet mat_the_basic_image_container.cpp init
The upper example shows how to create a matrix with more than two dimensions. Specify its
dimension, then pass a pointer containing the size for each dimension and the rest remains the
same.
- @ref cv::Mat::create function:
@code
M.create(4,4, CV_8UC(2));
cout << "M = "<< endl << " " << M << endl << endl;
@endcode
@snippet mat_the_basic_image_container.cpp create
![](images/MatBasicContainerOut2.png)
@@ -194,30 +188,26 @@ object in multiple ways:
- MATLAB style initializer: @ref cv::Mat::zeros , @ref cv::Mat::ones , @ref cv::Mat::eye . Specify size and
data type to use:
@skip Mat E
@until cout
@snippet mat_the_basic_image_container.cpp matlab
![](images/MatBasicContainerOut3.png)
- For small matrices you may use comma separated initializers:
@skip Mat C
@until cout
@snippet mat_the_basic_image_container.cpp comma
![](images/MatBasicContainerOut6.png)
- Create a new header for an existing *Mat* object and @ref cv::Mat::clone or @ref cv::Mat::copyTo it.
@skip Mat RowClone
@until cout
@snippet mat_the_basic_image_container.cpp clone
![](images/MatBasicContainerOut7.png)
@note
You can fill out a matrix with random values using the @ref cv::randu() function. You need to
give the lower and upper value for the random values:
@skip Mat R
@until randu
@snippet mat_the_basic_image_container.cpp random
Output formatting
@@ -227,25 +217,23 @@ In the above examples you could see the default formatting option. OpenCV, howev
format your matrix output:
- Default
@skipline (default)
@snippet mat_the_basic_image_container.cpp out-default
![](images/MatBasicContainerOut8.png)
- Python
@skipline (python)
@snippet mat_the_basic_image_container.cpp out-python
![](images/MatBasicContainerOut16.png)
- Comma separated values (CSV)
@skipline (csv)
@snippet mat_the_basic_image_container.cpp out-csv
![](images/MatBasicContainerOut10.png)
- Numpy
@code
cout << "R (numpy) = " << endl << format(R, Formatter::FMT_NUMPY ) << endl << endl;
@endcode
@snippet mat_the_basic_image_container.cpp out-numpy
![](images/MatBasicContainerOut9.png)
- C
@skipline (c)
@snippet mat_the_basic_image_container.cpp out-c
![](images/MatBasicContainerOut11.png)
Output of other common items
@@ -254,27 +242,23 @@ Output of other common items
OpenCV offers support for output of other common OpenCV data structures too via the \<\< operator:
- 2D Point
@skip Point2f P
@until cout
@snippet mat_the_basic_image_container.cpp out-point2
![](images/MatBasicContainerOut12.png)
- 3D Point
@skip Point3f P3f
@until cout
@snippet mat_the_basic_image_container.cpp out-point3
![](images/MatBasicContainerOut13.png)
- std::vector via cv::Mat
@skip vector<float> v
@until cout
@snippet mat_the_basic_image_container.cpp out-vector
![](images/MatBasicContainerOut14.png)
- std::vector of points
@skip vector<Point2f> vPoints
@until cout
@snippet mat_the_basic_image_container.cpp out-vector-points
![](images/MatBasicContainerOut15.png)
Most of the samples here have been included in a small console application. You can download it from
[here](samples/cpp/tutorial_code/core/mat_the_basic_image_container/mat_the_basic_image_container.cpp)
[here](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/core/mat_the_basic_image_container/mat_the_basic_image_container.cpp)
or in the core section of the cpp samples.
You can also find a quick video demonstration of this on