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Commit 0e7ca71d authored by Andrey Kamaev's avatar Andrey Kamaev
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Normalize whitespace in documentation and text files

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3rdparty/ffmpeg/readme.txt
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......@@ -16,7 +16,7 @@ How to update opencv_ffmpeg.dll and opencv_ffmpeg_64.dll when a new version of F
2. Install 64-bit MinGW. http://mingw-w64.sourceforge.net/
Let's assume, it's installed in C:\MSYS64
3. Copy C:\MSYS32\msys to C:\MSYS64\msys. Edit C:\MSYS64\msys\etc\fstab, change C:\MSYS32 to C:\MSYS64.
4. Now you have working MSYS32 and MSYS64 environments.
Launch, one by one, C:\MSYS32\msys\msys.bat and C:\MSYS64\msys\msys.bat to create your home directories.
......
3rdparty/readme.txt
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......@@ -45,13 +45,13 @@ jasper-1.900.1 - JasPer is a collection of software
and manipulation of images. This software can handle image data in a
variety of formats. One such format supported by JasPer is the JPEG-2000
format defined in ISO/IEC 15444-1.
Copyright (c) 1999-2000 Image Power, Inc.
Copyright (c) 1999-2000 The University of British Columbia
Copyright (c) 2001-2003 Michael David Adams
The JasPer license can be found in src/libjasper.
OpenCV on Windows uses pre-built libjasper library
(lib/libjasper*). To get the latest source code,
please, visit the project homepage:
......
android/java.rst
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......@@ -3,4 +3,4 @@ Java API
********
`Java API reference external link (JavaDoc) <http://docs.opencv.org/java/>`_
\ No newline at end of file
`Java API reference external link (JavaDoc) <http://docs.opencv.org/java/>`_
\ No newline at end of file
android/service/ReadMe.txt
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......@@ -8,7 +8,7 @@ The package provides new OpenCV SDK that uses OpenCV Manager for library initial
* Hardware specific optimizations for all supported platforms;
* Trusted OpenCV library source. All packages with OpenCV are published on Google Play service;
* Regular updates and bug fixes;
Package consists from Library Project for Java development with Eclipse, C++ headers and libraries for native application development, javadoc samples and prebuilt binaries for ARM and X86 platforms.
To try new SDK on serial device with Google Play just install sample package and follow application messages (Google Play service access will be needed).
TO start example on device without Google Play you need to install OpenCV manager package and OpenCV binary pack for your platform from apk folder before.
......
android/service/doc/BaseLoaderCallback.rst
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......@@ -55,6 +55,6 @@ There is a very base code snippet implementing the async initialization with Bas
Using in Service
----------------
Default BaseLoaderCallback implementation treat application context as Activity and calls Activity.finish() method to exit in case of initialization failure.
To override this behavior you need to override finish() method of BaseLoaderCallback class and implement your own finalization method.
Default BaseLoaderCallback implementation treat application context as Activity and calls Activity.finish() method to exit in case of initialization failure.
To override this behavior you need to override finish() method of BaseLoaderCallback class and implement your own finalization method.
android/service/doc/LoaderCallbackInterface.rst
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......@@ -13,7 +13,7 @@ void onManagerConnected()
.. method:: void onManagerConnected(int status)
Callback method that is called after OpenCV Library initialization.
:param status: status of initialization (see Initialization Status Constants).
void onPackageInstall()
......
doc/packaging.txt
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......@@ -4,14 +4,14 @@ INSTRUCTIONS TO BUILD WIN32 PACKAGES WITH CMAKE+CPACK
- Install NSIS.
- Generate OpenCV solutions for MSVC using CMake as usual.
- In cmake-gui:
- Mark BUILD_PACKAGE
- Mark BUILD_EXAMPLES (If examples are desired to be shipped as binaries...)
- Unmark ENABLE_OPENMP, since this feature seems to have some issues yet...
- Mark INSTALL_*_EXAMPLES
- In cmake-gui:
- Mark BUILD_PACKAGE
- Mark BUILD_EXAMPLES (If examples are desired to be shipped as binaries...)
- Unmark ENABLE_OPENMP, since this feature seems to have some issues yet...
- Mark INSTALL_*_EXAMPLES
- Open the OpenCV solution and build ALL in Debug and Release.
- Build PACKAGE, from the Release configuration. An NSIS installer package will be
- Build PACKAGE, from the Release configuration. An NSIS installer package will be
created with both release and debug LIBs and DLLs.
Jose Luis Blanco, 2009/JUL/29
doc/tutorials/calib3d/camera_calibration_square_chess/camera_calibration_square_chess.rst
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......@@ -7,16 +7,16 @@ Camera calibration with square chessboard
The goal of this tutorial is to learn how to calibrate a camera given a set of chessboard images.
*Test data*: use images in your data/chess folder.
*Test data*: use images in your data/chess folder.
#.
Compile opencv with samples by setting ``BUILD_EXAMPLES`` to ``ON`` in cmake configuration.
Compile opencv with samples by setting ``BUILD_EXAMPLES`` to ``ON`` in cmake configuration.
#.
Go to ``bin`` folder and use ``imagelist_creator`` to create an ``XML/YAML`` list of your images.
#.
Then, run ``calibration`` sample to get camera parameters. Use square size equal to 3cm.
Then, run ``calibration`` sample to get camera parameters. Use square size equal to 3cm.
Pose estimation
===============
......@@ -57,6 +57,6 @@ Now, let us write a code that detects a chessboard in a new image and finds its
distCoeffs, rvec, tvec, false);
#.
Calculate reprojection error like it is done in ``calibration`` sample (see ``opencv/samples/cpp/calibration.cpp``, function ``computeReprojectionErrors``).
Calculate reprojection error like it is done in ``calibration`` sample (see ``opencv/samples/cpp/calibration.cpp``, function ``computeReprojectionErrors``).
Question: how to calculate the distance from the camera origin to any of the corners?
\ No newline at end of file
Question: how to calculate the distance from the camera origin to any of the corners?
\ No newline at end of file
doc/tutorials/calib3d/table_of_content_calib3d/table_of_content_calib3d.rst
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......@@ -3,11 +3,11 @@
*calib3d* module. Camera calibration and 3D reconstruction
-----------------------------------------------------------
Although we got most of our images in a 2D format they do come from a 3D world. Here you will learn how to find out from the 2D images information about the 3D world.
Although we got most of our images in a 2D format they do come from a 3D world. Here you will learn how to find out from the 2D images information about the 3D world.
.. include:: ../../definitions/tocDefinitions.rst
.. include:: ../../definitions/tocDefinitions.rst
+
+
.. tabularcolumns:: m{100pt} m{300pt}
.. cssclass:: toctableopencv
......@@ -26,7 +26,7 @@ Although we got most of our images in a 2D format they do come from a 3D world.
:height: 90pt
:width: 90pt
+
+
.. tabularcolumns:: m{100pt} m{300pt}
.. cssclass:: toctableopencv
......
doc/tutorials/core/adding_images/adding_images.rst
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......@@ -18,7 +18,7 @@ Theory
.. note::
The explanation below belongs to the book `Computer Vision: Algorithms and Applications <http://szeliski.org/Book/>`_ by Richard Szeliski
The explanation below belongs to the book `Computer Vision: Algorithms and Applications <http://szeliski.org/Book/>`_ by Richard Szeliski
From our previous tutorial, we know already a bit of *Pixel operators*. An interesting dyadic (two-input) operator is the *linear blend operator*:
......@@ -43,7 +43,7 @@ As usual, after the not-so-lengthy explanation, let's go to the code:
int main( int argc, char** argv )
{
double alpha = 0.5; double beta; double input;
double alpha = 0.5; double beta; double input;
Mat src1, src2, dst;
......@@ -69,7 +69,7 @@ As usual, after the not-so-lengthy explanation, let's go to the code:
beta = ( 1.0 - alpha );
addWeighted( src1, alpha, src2, beta, 0.0, dst);
imshow( "Linear Blend", dst );
waitKey(0);
......@@ -99,10 +99,10 @@ Explanation
#. Now we need to generate the :math:`g(x)` image. For this, the function :add_weighted:`addWeighted <>` comes quite handy:
.. code-block:: cpp
beta = ( 1.0 - alpha );
addWeighted( src1, alpha, src2, beta, 0.0, dst);
since :add_weighted:`addWeighted <>` produces:
.. math::
......@@ -110,12 +110,12 @@ Explanation
dst = \alpha \cdot src1 + \beta \cdot src2 + \gamma
In this case, :math:`\gamma` is the argument :math:`0.0` in the code above.
#. Create windows, show the images and wait for the user to end the program.
#. Create windows, show the images and wait for the user to end the program.
Result
=======
.. image:: images/Adding_Images_Tutorial_Result_0.jpg
:alt: Blending Images Tutorial - Final Result
:align: center
:align: center
doc/tutorials/core/basic_geometric_drawing/basic_geometric_drawing.rst
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......@@ -99,11 +99,11 @@ Explanation
/// 2.b. Creating rectangles
rectangle( rook_image,
Point( 0, 7*w/8.0 ),
Point( w, w),
Scalar( 0, 255, 255 ),
-1,
8 );
Point( 0, 7*w/8.0 ),
Point( w, w),
Scalar( 0, 255, 255 ),
-1,
8 );
/// 2.c. Create a few lines
MyLine( rook_image, Point( 0, 15*w/16 ), Point( w, 15*w/16 ) );
......@@ -118,16 +118,16 @@ Explanation
.. code-block:: cpp
void MyLine( Mat img, Point start, Point end )
{
int thickness = 2;
int lineType = 8;
line( img,
start,
end,
Scalar( 0, 0, 0 ),
thickness,
lineType );
}
{
int thickness = 2;
int lineType = 8;
line( img,
start,
end,
Scalar( 0, 0, 0 ),
thickness,
lineType );
}
As we can see, *MyLine* just call the function :line:`line <>`, which does the following:
......@@ -145,18 +145,18 @@ Explanation
void MyEllipse( Mat img, double angle )
{
int thickness = 2;
int lineType = 8;
ellipse( img,
Point( w/2.0, w/2.0 ),
Size( w/4.0, w/16.0 ),
angle,
0,
360,
Scalar( 255, 0, 0 ),
thickness,
lineType );
int thickness = 2;
int lineType = 8;
ellipse( img,
Point( w/2.0, w/2.0 ),
Size( w/4.0, w/16.0 ),
angle,
0,
360,
Scalar( 255, 0, 0 ),
thickness,
lineType );
}
From the code above, we can observe that the function :ellipse:`ellipse <>` draws an ellipse such that:
......@@ -176,17 +176,17 @@ Explanation
.. code-block:: cpp
void MyFilledCircle( Mat img, Point center )
{
int thickness = -1;
int lineType = 8;
circle( img,
center,
w/32.0,
Scalar( 0, 0, 255 ),
thickness,
lineType );
}
{
int thickness = -1;
int lineType = 8;
circle( img,
center,
w/32.0,
Scalar( 0, 0, 255 ),
thickness,
lineType );
}
Similar to the ellipse function, we can observe that *circle* receives as arguments:
......@@ -203,41 +203,41 @@ Explanation
.. code-block:: cpp
void MyPolygon( Mat img )
{
int lineType = 8;
/** Create some points */
Point rook_points[1][20];
rook_points[0][0] = Point( w/4.0, 7*w/8.0 );
rook_points[0][1] = Point( 3*w/4.0, 7*w/8.0 );
rook_points[0][2] = Point( 3*w/4.0, 13*w/16.0 );
rook_points[0][3] = Point( 11*w/16.0, 13*w/16.0 );
rook_points[0][4] = Point( 19*w/32.0, 3*w/8.0 );
rook_points[0][5] = Point( 3*w/4.0, 3*w/8.0 );
rook_points[0][6] = Point( 3*w/4.0, w/8.0 );
rook_points[0][7] = Point( 26*w/40.0, w/8.0 );
rook_points[0][8] = Point( 26*w/40.0, w/4.0 );
rook_points[0][9] = Point( 22*w/40.0, w/4.0 );
rook_points[0][10] = Point( 22*w/40.0, w/8.0 );
rook_points[0][11] = Point( 18*w/40.0, w/8.0 );
rook_points[0][12] = Point( 18*w/40.0, w/4.0 );
rook_points[0][13] = Point( 14*w/40.0, w/4.0 );
rook_points[0][14] = Point( 14*w/40.0, w/8.0 );
rook_points[0][15] = Point( w/4.0, w/8.0 );
rook_points[0][16] = Point( w/4.0, 3*w/8.0 );
rook_points[0][17] = Point( 13*w/32.0, 3*w/8.0 );
rook_points[0][18] = Point( 5*w/16.0, 13*w/16.0 );
rook_points[0][19] = Point( w/4.0, 13*w/16.0) ;
const Point* ppt[1] = { rook_points[0] };
int npt[] = { 20 };
fillPoly( img,
ppt,
npt,
1,
Scalar( 255, 255, 255 ),
lineType );
{
int lineType = 8;
/** Create some points */
Point rook_points[1][20];
rook_points[0][0] = Point( w/4.0, 7*w/8.0 );
rook_points[0][1] = Point( 3*w/4.0, 7*w/8.0 );
rook_points[0][2] = Point( 3*w/4.0, 13*w/16.0 );
rook_points[0][3] = Point( 11*w/16.0, 13*w/16.0 );
rook_points[0][4] = Point( 19*w/32.0, 3*w/8.0 );
rook_points[0][5] = Point( 3*w/4.0, 3*w/8.0 );
rook_points[0][6] = Point( 3*w/4.0, w/8.0 );
rook_points[0][7] = Point( 26*w/40.0, w/8.0 );
rook_points[0][8] = Point( 26*w/40.0, w/4.0 );
rook_points[0][9] = Point( 22*w/40.0, w/4.0 );
rook_points[0][10] = Point( 22*w/40.0, w/8.0 );
rook_points[0][11] = Point( 18*w/40.0, w/8.0 );
rook_points[0][12] = Point( 18*w/40.0, w/4.0 );
rook_points[0][13] = Point( 14*w/40.0, w/4.0 );
rook_points[0][14] = Point( 14*w/40.0, w/8.0 );
rook_points[0][15] = Point( w/4.0, w/8.0 );
rook_points[0][16] = Point( w/4.0, 3*w/8.0 );
rook_points[0][17] = Point( 13*w/32.0, 3*w/8.0 );
rook_points[0][18] = Point( 5*w/16.0, 13*w/16.0 );
rook_points[0][19] = Point( w/4.0, 13*w/16.0) ;
const Point* ppt[1] = { rook_points[0] };
int npt[] = { 20 };
fillPoly( img,
ppt,
npt,
1,
Scalar( 255, 255, 255 ),
lineType );
}
To draw a filled polygon we use the function :fill_poly:`fillPoly <>`. We note that:
......@@ -255,11 +255,11 @@ Explanation
.. code-block:: cpp
rectangle( rook_image,
Point( 0, 7*w/8.0 ),
Point( w, w),
Scalar( 0, 255, 255 ),
-1,
8 );
Point( 0, 7*w/8.0 ),
Point( w, w),
Scalar( 0, 255, 255 ),
-1,
8 );
Finally we have the :rectangle:`rectangle <>` function (we did not create a special function for this guy). We note that:
......
doc/tutorials/core/basic_linear_transform/basic_linear_transform.rst
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......@@ -10,7 +10,7 @@ In this tutorial you will learn how to:
.. container:: enumeratevisibleitemswithsquare
+ Access pixel values
+ Access pixel values
+ Initialize a matrix with zeros
......@@ -20,16 +20,16 @@ In this tutorial you will learn how to:
Theory
=======
.. note::
The explanation below belongs to the book `Computer Vision: Algorithms and Applications <http://szeliski.org/Book/>`_ by Richard Szeliski
The explanation below belongs to the book `Computer Vision: Algorithms and Applications <http://szeliski.org/Book/>`_ by Richard Szeliski
Image Processing
--------------------
.. container:: enumeratevisibleitemswithsquare
* A general image processing operator is a function that takes one or more input images and produces an output image.
* A general image processing operator is a function that takes one or more input images and produces an output image.
* Image transforms can be seen as:
......@@ -54,18 +54,18 @@ Brightness and contrast adjustments
* Two commonly used point processes are *multiplication* and *addition* with a constant:
.. math::
g(x) = \alpha f(x) + \beta
* The parameters :math:`\alpha > 0` and :math:`\beta` are often called the *gain* and *bias* parameters; sometimes these parameters are said to control *contrast* and *brightness* respectively.
* You can think of :math:`f(x)` as the source image pixels and :math:`g(x)` as the output image pixels. Then, more conveniently we can write the expression as:
.. math::
g(i,j) = \alpha \cdot f(i,j) + \beta
where :math:`i` and :math:`j` indicates that the pixel is located in the *i-th* row and *j-th* column.
where :math:`i` and :math:`j` indicates that the pixel is located in the *i-th* row and *j-th* column.
Code
=====
......@@ -91,7 +91,7 @@ Code
Mat image = imread( argv[1] );
Mat new_image = Mat::zeros( image.size(), image.type() );
/// Initialize values
/// Initialize values
std::cout<<" Basic Linear Transforms "<<std::endl;
std::cout<<"-------------------------"<<std::endl;
std::cout<<"* Enter the alpha value [1.0-3.0]: ";std::cin>>alpha;
......@@ -102,7 +102,7 @@ Code
{ for( int x = 0; x < image.cols; x++ )
{ for( int c = 0; c < 3; c++ )
{
new_image.at<Vec3b>(y,x)[c] =
new_image.at<Vec3b>(y,x)[c] =
saturate_cast<uchar>( alpha*( image.at<Vec3b>(y,x)[c] ) + beta );
}
}
......@@ -133,41 +133,41 @@ Explanation
#. We load an image using :imread:`imread <>` and save it in a Mat object:
.. code-block:: cpp
Mat image = imread( argv[1] );
#. Now, since we will make some transformations to this image, we need a new Mat object to store it. Also, we want this to have the following features:
.. container:: enumeratevisibleitemswithsquare
* Initial pixel values equal to zero
* Same size and type as the original image
.. code-block:: cpp
Mat new_image = Mat::zeros( image.size(), image.type() );
We observe that :mat_zeros:`Mat::zeros <>` returns a Matlab-style zero initializer based on *image.size()* and *image.type()*
Mat new_image = Mat::zeros( image.size(), image.type() );
We observe that :mat_zeros:`Mat::zeros <>` returns a Matlab-style zero initializer based on *image.size()* and *image.type()*
#. Now, to perform the operation :math:`g(i,j) = \alpha \cdot f(i,j) + \beta` we will access to each pixel in image. Since we are operating with RGB images, we will have three values per pixel (R, G and B), so we will also access them separately. Here is the piece of code:
.. code-block:: cpp
for( int y = 0; y < image.rows; y++ )
{ for( int x = 0; x < image.cols; x++ )
{ for( int c = 0; c < 3; c++ )
{ new_image.at<Vec3b>(y,x)[c] =
{ new_image.at<Vec3b>(y,x)[c] =
saturate_cast<uchar>( alpha*( image.at<Vec3b>(y,x)[c] ) + beta ); }
}
}
Notice the following:
.. container:: enumeratevisibleitemswithsquare
* To access each pixel in the images we are using this syntax: *image.at<Vec3b>(y,x)[c]* where *y* is the row, *x* is the column and *c* is R, G or B (0, 1 or 2).
* To access each pixel in the images we are using this syntax: *image.at<Vec3b>(y,x)[c]* where *y* is the row, *x* is the column and *c* is R, G or B (0, 1 or 2).
* Since the operation :math:`\alpha \cdot p(i,j) + \beta` can give values out of range or not integers (if :math:`\alpha` is float), we use :saturate_cast:`saturate_cast <>` to make sure the values are valid.
......@@ -175,7 +175,7 @@ Explanation
#. Finally, we create windows and show the images, the usual way.
.. code-block:: cpp
namedWindow("Original Image", 1);
namedWindow("New Image", 1);
......@@ -185,9 +185,9 @@ Explanation
waitKey(0);
.. note::
Instead of using the **for** loops to access each pixel, we could have simply used this command:
.. code-block:: cpp
image.convertTo(new_image, -1, alpha, beta);
......@@ -211,4 +211,4 @@ Result
.. image:: images/Basic_Linear_Transform_Tutorial_Result_0.jpg
:alt: Basic Linear Transform - Final Result
:align: center
:align: center
doc/tutorials/core/file_input_output_with_xml_yml/file_input_output_with_xml_yml.rst
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......@@ -4,9 +4,9 @@ File Input and Output using XML and YAML files
**********************************************
Goal
====
====
You'll find answers for the following questions:
You'll find answers for the following questions:
.. container:: enumeratevisibleitemswithsquare
......@@ -18,7 +18,7 @@ You'll find answers for the following questions:
Source code
===========
You can :download:`download this from here <../../../../samples/cpp/tutorial_code/core/file_input_output/file_input_output.cpp>` or find it in the :file:`samples/cpp/tutorial_code/core/file_input_output/file_input_output.cpp` of the OpenCV source code library.
You can :download:`download this from here <../../../../samples/cpp/tutorial_code/core/file_input_output/file_input_output.cpp>` or find it in the :file:`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.
......@@ -31,9 +31,9 @@ Here's a sample code of how to achieve all the stuff enumerated at the goal list
Explanation
===========
Here we talk only about XML and YAML file inputs. Your output (and its respective input) file may have only one of these extensions and the structure coming from this. They are two kinds of data structures you may serialize: *mappings* (like the STL map) and *element sequence* (like the STL vector>. The difference between these is that in a map every element has a unique name through what you may access it. For sequences you need to go through them to query a specific item.
Here we talk only about XML and YAML file inputs. Your output (and its respective input) file may have only one of these extensions and the structure coming from this. They are two kinds of data structures you may serialize: *mappings* (like the STL map) and *element sequence* (like the STL vector>. The difference between these is that in a map every element has a unique name through what you may access it. For sequences you need to go through them to query a specific item.
1. **XML\\YAML File Open and Close.** Before you write any content to such file you need to open it and at the end to close it. The XML\YAML data structure in OpenCV is :xmlymlpers:`FileStorage <filestorage>`. To specify that this structure to which file binds on your hard drive you can use either its constructor or the *open()* function of this:
1. **XML\\YAML File Open and Close.** Before you write any content to such file you need to open it and at the end to close it. The XML\YAML data structure in OpenCV is :xmlymlpers:`FileStorage <filestorage>`. To specify that this structure to which file binds on your hard drive you can use either its constructor or the *open()* function of this:
.. code-block:: cpp
......@@ -42,29 +42,29 @@ Here we talk only about XML and YAML file inputs. Your output (and its respectiv
\\...
fs.open(filename, FileStorage::READ);
Either one of this you use the second argument is a constant specifying the type of operations you'll be able to on them: WRITE, READ or APPEND. The extension specified in the file name also determinates the output format that will be used. The output may be even compressed if you specify an extension such as *.xml.gz*.
Either one of this you use the second argument is a constant specifying the type of operations you'll be able to on them: WRITE, READ or APPEND. The extension specified in the file name also determinates the output format that will be used. The output may be even compressed if you specify an extension such as *.xml.gz*.
The file automatically closes when the :xmlymlpers:`FileStorage <filestorage>` objects is destroyed. However, you may explicitly call for this by using the *release* function:
The file automatically closes when the :xmlymlpers:`FileStorage <filestorage>` objects is destroyed. However, you may explicitly call for this by using the *release* function:
.. code-block:: cpp
fs.release(); // explicit close
#. **Input and Output of text and numbers.** The data structure uses the same << output operator that the STL library. For outputting any type of data structure we need first to specify its name. We do this by just simply printing out the name of this. For basic types you may follow this with the print of the value :
#. **Input and Output of text and numbers.** The data structure uses the same << output operator that the STL library. For outputting any type of data structure we need first to specify its name. We do this by just simply printing out the name of this. For basic types you may follow this with the print of the value :
.. code-block:: cpp
fs << "iterationNr" << 100;
Reading in is a simple addressing (via the [] operator) and casting operation or a read via the >> operator :
Reading in is a simple addressing (via the [] operator) and casting operation or a read via the >> operator :
.. code-block:: cpp
int itNr;
int itNr;
fs["iterationNr"] >> itNr;
itNr = (int) fs["iterationNr"];
#. **Input\\Output of OpenCV Data structures.** Well these behave exactly just as the basic C++ types:
#. **Input\\Output of OpenCV Data structures.** Well these behave exactly just as the basic C++ types:
.. code-block:: cpp
......@@ -77,7 +77,7 @@ Here we talk only about XML and YAML file inputs. Your output (and its respectiv
fs["R"] >> R; // Read cv::Mat
fs["T"] >> T;
#. **Input\\Output of vectors (arrays) and associative maps.** As I mentioned beforehand we can output maps and sequences (array, vector) too. Again we first print the name of the variable and then we have to specify if our output is either a sequence or map.
#. **Input\\Output of vectors (arrays) and associative maps.** As I mentioned beforehand we can output maps and sequences (array, vector) too. Again we first print the name of the variable and then we have to specify if our output is either a sequence or map.
For sequence before the first element print the "[" character and after the last one the "]" character:
......@@ -95,7 +95,7 @@ Here we talk only about XML and YAML file inputs. Your output (and its respectiv
fs << "{" << "One" << 1;
fs << "Two" << 2 << "}";
To read from these we use the :xmlymlpers:`FileNode <filenode>` and the :xmlymlpers:`FileNodeIterator <filenodeiterator>` data structures. The [] operator of the :xmlymlpers:`FileStorage <filestorage>` class returns a :xmlymlpers:`FileNode <filenode>` data type. If the node is sequential we can use the :xmlymlpers:`FileNodeIterator <filenodeiterator>` to iterate through the items:
To read from these we use the :xmlymlpers:`FileNode <filenode>` and the :xmlymlpers:`FileNodeIterator <filenodeiterator>` data structures. The [] operator of the :xmlymlpers:`FileStorage <filestorage>` class returns a :xmlymlpers:`FileNode <filenode>` data type. If the node is sequential we can use the :xmlymlpers:`FileNodeIterator <filenodeiterator>` to iterate through the items:
.. code-block:: cpp
......@@ -115,8 +115,8 @@ Here we talk only about XML and YAML file inputs. Your output (and its respectiv
.. code-block:: cpp
n = fs["Mapping"]; // Read mappings from a sequence
cout << "Two " << (int)(n["Two"]) << "; ";
cout << "One " << (int)(n["One"]) << endl << endl;
cout << "Two " << (int)(n["Two"]) << "; ";
cout << "One " << (int)(n["One"]) << endl << endl;
#. **Read and write your own data structures.** Suppose you have a data structure such as:
......@@ -148,7 +148,7 @@ Here we talk only about XML and YAML file inputs. Your output (and its respectiv
id = (string)node["id"];
}
Then you need to add the following functions definitions outside the class:
Then you need to add the following functions definitions outside the class:
.. code-block:: cpp
......@@ -175,17 +175,17 @@ Here we talk only about XML and YAML file inputs. Your output (and its respectiv
fs << "MyData" << m; // your own data structures
fs["MyData"] >> m; // Read your own structure_
Or to try out reading a non-existing read:
Or to try out reading a non-existing read:
.. code-block:: cpp
fs["NonExisting"] >> m; // Do not add a fs << "NonExisting" << m command for this to work
fs["NonExisting"] >> m; // Do not add a fs << "NonExisting" << m command for this to work
cout << endl << "NonExisting = " << endl << m << endl;
Result
======
Well mostly we just print out the defined numbers. On the screen of your console you could see:
Well mostly we just print out the defined numbers. On the screen of your console you could see:
.. code-block:: bash
......@@ -212,7 +212,7 @@ Well mostly we just print out the defined numbers. On the screen of your console
Tip: Open up output.xml with a text editor to see the serialized data.
Nevertheless, it's much more interesting what you may see in the output xml file:
Nevertheless, it's much more interesting what you may see in the output xml file:
.. code-block:: xml
......@@ -242,7 +242,7 @@ Nevertheless, it's much more interesting what you may see in the output xml file
<id>mydata1234</id></MyData>
</opencv_storage>
Or the YAML file:
Or the YAML file:
.. code-block:: yaml
......
doc/tutorials/core/mat-mask-operations/mat-mask-operations.rst
View file @ 0e7ca71d
......@@ -8,11 +8,11 @@ Mask operations on matrices are quite simple. The idea is that we recalculate ea
Our test case
=============
Let us consider the issue of an image contrast enhancement method. Basically we want to apply for every pixel of the image the following formula:
Let us consider the issue of an image contrast enhancement method. Basically we want to apply for every pixel of the image the following formula:
.. math::
I(i,j) = 5*I(i,j) - [ I(i-1,j) + I(i+1,j) + I(i,j-1) + I(i,j+1)]
I(i,j) = 5*I(i,j) - [ I(i-1,j) + I(i+1,j) + I(i,j-1) + I(i,j+1)]
\iff I(i,j)*M, \text{where }
M = \bordermatrix{ _i\backslash ^j & -1 & 0 & +1 \cr
......@@ -23,12 +23,12 @@ Let us consider the issue of an image contrast enhancement method. Basically we
The first notation is by using a formula, while the second is a compacted version of the first by using a mask. You use the mask by putting the center of the mask matrix (in the upper case noted by the zero-zero index) on the pixel you want to calculate and sum up the pixel values multiplied with the overlapped matrix values. It's the same thing, however in case of large matrices the latter notation is a lot easier to look over.
Now let us see how we can make this happen by using the basic pixel access method or by using the :filtering:`filter2D <filter2d>` function.
Now let us see how we can make this happen by using the basic pixel access method or by using the :filtering:`filter2D <filter2d>` function.
The Basic Method
================
Here's a function that will do this:
Here's a function that will do this:
.. code-block:: cpp
......@@ -49,7 +49,7 @@ Here's a function that will do this:
for(int i= nChannels;i < nChannels*(myImage.cols-1); ++i)
{
*output++ = saturate_cast<uchar>(5*current[i]
*output++ = saturate_cast<uchar>(5*current[i]
-current[i-nChannels] - current[i+nChannels] - previous[i] - next[i]);
}
}
......@@ -87,7 +87,7 @@ We'll use the plain C [] operator to access pixels. Because we need to access mu
for(int i= nChannels;i < nChannels*(myImage.cols-1); ++i)
{
*output++ = saturate_cast<uchar>(5*current[i]
*output++ = saturate_cast<uchar>(5*current[i]
-current[i-nChannels] - current[i+nChannels] - previous[i] - next[i]);
}
}
......@@ -96,7 +96,7 @@ On the borders of the image the upper notation results inexistent pixel location
.. code-block:: cpp
Result.row(0).setTo(Scalar(0)); // The top row
Result.row(0).setTo(Scalar(0)); // The top row
Result.row(Result.rows-1).setTo(Scalar(0)); // The bottom row
Result.col(0).setTo(Scalar(0)); // The left column
Result.col(Result.cols-1).setTo(Scalar(0)); // The right column
......@@ -108,19 +108,19 @@ Applying such filters are so common in image processing that in OpenCV there exi
.. code-block:: cpp
Mat kern = (Mat_<char>(3,3) << 0, -1, 0,
Mat kern = (Mat_<char>(3,3) << 0, -1, 0,
-1, 5, -1,
0, -1, 0);
Then call the :filtering:`filter2D <filter2d>` function specifying the input, the output image and the kernell to use:
Then call the :filtering:`filter2D <filter2d>` function specifying the input, the output image and the kernell to use:
.. code-block:: cpp
filter2D(I, K, I.depth(), kern );
filter2D(I, K, I.depth(), kern );
The function even has a fifth optional argument to specify the center of the kernel, and a sixth one for determining what to do in the regions where the operation is undefined (borders). Using this function has the advantage that it's shorter, less verbose and because there are some optimization techniques implemented it is usually faster than the *hand-coded method*. For example in my test while the second one took only 13 milliseconds the first took around 31 milliseconds. Quite some difference.
For example:
For example:
.. image:: images/resultMatMaskFilter2D.png
:alt: A sample output of the program
......@@ -128,7 +128,7 @@ For example:
You can download this source code from :download:`here <../../../../samples/cpp/tutorial_code/core/mat_mask_operations/mat_mask_operations.cpp>` or look in the OpenCV source code libraries sample directory at :file:`samples/cpp/tutorial_code/core/mat_mask_operations/mat_mask_operations.cpp`.
Check out an instance of running the program on our `YouTube channel <http://www.youtube.com/watch?v=7PF1tAU9se4>`_ .
Check out an instance of running the program on our `YouTube channel <http://www.youtube.com/watch?v=7PF1tAU9se4>`_ .
.. raw:: html
......
doc/tutorials/core/table_of_content_core/table_of_content_core.rst
View file @ 0e7ca71d
......@@ -44,7 +44,7 @@ Here you will learn the about the basic building blocks of the library. A must r
.. |HowScanImag| image:: images/howToScanImages.jpg
:height: 90pt
:width: 90pt
+
.. tabularcolumns:: m{100pt} m{300pt}
......@@ -193,7 +193,7 @@ Here you will learn the about the basic building blocks of the library. A must r
*Author:* |Author_BernatG|
Did you used OpenCV before its 2.0 version? Do you wanna know what happened with your library with 2.0? Don't you know how to convert your old OpenCV programs to the new C++ interface? Look here to shed light on all this questions.
=============== ======================================================
.. |InterOOpenCV1| image:: images/interopOpenCV1.png
......@@ -208,7 +208,7 @@ Here you will learn the about the basic building blocks of the library. A must r
.. toctree::
:hidden:
../mat_the_basic_image_container/mat_the_basic_image_container
../how_to_scan_images/how_to_scan_images
../mat-mask-operations/mat-mask-operations
......
doc/tutorials/definitions/noContent.rst
View file @ 0e7ca71d
.. note::
Unfortunetly we have no tutorials into this section. Nevertheless, our tutorial writting team is working on it. If you have a tutorial suggestion or you have writen yourself a tutorial (or coded a sample code) that you would like to see here please contact us via our :opencv_group:`user group <>`.
\ No newline at end of file
Unfortunetly we have no tutorials into this section. Nevertheless, our tutorial writting team is working on it. If you have a tutorial suggestion or you have writen yourself a tutorial (or coded a sample code) that you would like to see here please contact us via our :opencv_group:`user group <>`.
\ No newline at end of file
doc/tutorials/definitions/tocDefinitions.rst
View file @ 0e7ca71d
......@@ -3,8 +3,8 @@
.. |Author_AndreyK| unicode:: Andrey U+0020 Kamaev
.. |Author_LeonidBLB| unicode:: Leonid U+0020 Beynenson
.. |Author_VsevolodG| unicode:: Vsevolod U+0020 Glumov
.. |Author_VictorE| unicode:: Victor U+0020 Eruhimov
.. |Author_ArtemM| unicode:: Artem U+0020 Myagkov
.. |Author_FernandoI| unicode:: Fernando U+0020 Iglesias U+0020 Garc U+00ED a
.. |Author_VictorE| unicode:: Victor U+0020 Eruhimov
.. |Author_ArtemM| unicode:: Artem U+0020 Myagkov
.. |Author_FernandoI| unicode:: Fernando U+0020 Iglesias U+0020 Garc U+00ED a
.. |Author_EduardF| unicode:: Eduard U+0020 Feicho
doc/tutorials/features2d/detection_of_planar_objects/detection_of_planar_objects.rst
View file @ 0e7ca71d
......@@ -5,9 +5,9 @@ Detection of planar objects
.. highlight:: cpp
The goal of this tutorial is to learn how to use *features2d* and *calib3d* modules for detecting known planar objects in scenes.
The goal of this tutorial is to learn how to use *features2d* and *calib3d* modules for detecting known planar objects in scenes.
*Test data*: use images in your data folder, for instance, ``box.png`` and ``box_in_scene.png``.
*Test data*: use images in your data folder, for instance, ``box.png`` and ``box_in_scene.png``.
#.
Create a new console project. Read two input images. ::
......@@ -22,7 +22,7 @@ The goal of this tutorial is to learn how to use *features2d* and *calib3d* modu
FastFeatureDetector detector(15);
vector<KeyPoint> keypoints1;
detector.detect(img1, keypoints1);
... // do the same for the second image
#.
......@@ -32,7 +32,7 @@ The goal of this tutorial is to learn how to use *features2d* and *calib3d* modu
SurfDescriptorExtractor extractor;
Mat descriptors1;
extractor.compute(img1, keypoints1, descriptors1);
... // process keypoints from the second image as well
#.
......@@ -69,4 +69,4 @@ The goal of this tutorial is to learn how to use *features2d* and *calib3d* modu
perspectiveTransform(Mat(points1), points1Projected, H);
#.
Use ``drawMatches`` for drawing inliers.
Use ``drawMatches`` for drawing inliers.
doc/tutorials/features2d/table_of_content_features2d/table_of_content_features2d.rst
View file @ 0e7ca71d
......@@ -5,166 +5,166 @@
Learn about how to use the feature points detectors, descriptors and matching framework found inside OpenCV.
.. include:: ../../definitions/tocDefinitions.rst
.. include:: ../../definitions/tocDefinitions.rst
+
+
.. tabularcolumns:: m{100pt} m{300pt}
.. cssclass:: toctableopencv
===================== ==============================================
|Harris| **Title:** :ref:`harris_detector`
*Compatibility:* > OpenCV 2.0
*Author:* |Author_AnaH|
Why is it a good idea to track corners? We learn to use the Harris method to detect corners
===================== ==============================================
.. |Harris| image:: images/trackingmotion/Harris_Detector_Cover.jpg
:height: 90pt
:width: 90pt
+
+
.. tabularcolumns:: m{100pt} m{300pt}
.. cssclass:: toctableopencv
===================== ==============================================
|ShiTomasi| **Title:** :ref:`good_features_to_track`
*Compatibility:* > OpenCV 2.0
*Author:* |Author_AnaH|
Where we use an improved method to detect corners more accuratelyI
===================== ==============================================
.. |ShiTomasi| image:: images/trackingmotion/Shi_Tomasi_Detector_Cover.jpg
:height: 90pt
:width: 90pt
+
+
.. tabularcolumns:: m{100pt} m{300pt}
.. cssclass:: toctableopencv
===================== ==============================================
|GenericCorner| **Title:** :ref:`generic_corner_detector`
*Compatibility:* > OpenCV 2.0
*Author:* |Author_AnaH|
Here you will learn how to use OpenCV functions to make your personalized corner detector!
===================== ==============================================
.. |GenericCorner| image:: images/trackingmotion/Generic_Corner_Detector_Cover.jpg
:height: 90pt
:width: 90pt
+
+
.. tabularcolumns:: m{100pt} m{300pt}
.. cssclass:: toctableopencv
===================== ==============================================
|Subpixel| **Title:** :ref:`corner_subpixeles`
*Compatibility:* > OpenCV 2.0
*Author:* |Author_AnaH|
Is pixel resolution enough? Here we learn a simple method to improve our accuracy.
===================== ==============================================
.. |Subpixel| image:: images/trackingmotion/Corner_Subpixeles_Cover.jpg
:height: 90pt
:width: 90pt
+
+
.. tabularcolumns:: m{100pt} m{300pt}
.. cssclass:: toctableopencv
===================== ==============================================
|FeatureDetect| **Title:** :ref:`feature_detection`
*Compatibility:* > OpenCV 2.0
*Author:* |Author_AnaH|
In this tutorial, you will use *features2d* to detect interest points.
===================== ==============================================
.. |FeatureDetect| image:: images/Feature_Detection_Tutorial_Cover.jpg
:height: 90pt
:width: 90pt
+
+
.. tabularcolumns:: m{100pt} m{300pt}
.. cssclass:: toctableopencv
===================== ==============================================
|FeatureDescript| **Title:** :ref:`feature_description`
*Compatibility:* > OpenCV 2.0
*Author:* |Author_AnaH|
In this tutorial, you will use *features2d* to calculate feature vectors.
===================== ==============================================
.. |FeatureDescript| image:: images/Feature_Description_Tutorial_Cover.jpg
:height: 90pt
:width: 90pt
+
+
.. tabularcolumns:: m{100pt} m{300pt}
.. cssclass:: toctableopencv
===================== ==============================================
|FeatureFlann| **Title:** :ref:`feature_flann_matcher`
*Compatibility:* > OpenCV 2.0
*Author:* |Author_AnaH|
In this tutorial, you will use the FLANN library to make a fast matching.
===================== ==============================================
.. |FeatureFlann| image:: images/Feature_Flann_Matcher_Tutorial_Cover.jpg
:height: 90pt
:width: 90pt
+
+
.. tabularcolumns:: m{100pt} m{300pt}
.. cssclass:: toctableopencv
===================== ==============================================
|FeatureHomo| **Title:** :ref:`feature_homography`
*Compatibility:* > OpenCV 2.0
*Author:* |Author_AnaH|
In this tutorial, you will use *features2d* and *calib3d* to detect an object in a scene.
===================== ==============================================
.. |FeatureHomo| image:: images/Feature_Homography_Tutorial_Cover.jpg
:height: 90pt
:width: 90pt
+
+
.. tabularcolumns:: m{100pt} m{300pt}
.. cssclass:: toctableopencv
......@@ -175,7 +175,7 @@ Learn about how to use the feature points detectors, descriptors and matching f
*Author:* |Author_VictorE|
You will use *features2d* and *calib3d* modules for detecting known planar objects in scenes.
You will use *features2d* and *calib3d* modules for detecting known planar objects in scenes.
===================== ==============================================
......
doc/tutorials/features2d/trackingmotion/corner_subpixeles/corner_subpixeles.rst
View file @ 0e7ca71d
......@@ -87,14 +87,14 @@ This tutorial code's is shown lines below. You can also download it from `here <
/// Apply corner detection
goodFeaturesToTrack( src_gray,
corners,
maxCorners,
qualityLevel,
minDistance,
Mat(),
blockSize,
useHarrisDetector,
k );
corners,
maxCorners,
qualityLevel,
minDistance,
Mat(),
blockSize,
useHarrisDetector,
k );
/// Draw corners detected
......
doc/tutorials/features2d/trackingmotion/harris_detector/harris_detector.rst
View file @ 0e7ca71d
......@@ -98,16 +98,16 @@ How does it work?
u & v
\end{bmatrix}
\left (
\displaystyle \sum_{x,y}
\displaystyle \sum_{x,y}
w(x,y)
\begin{bmatrix}
I_x^{2} & I_{x}I_{y} \\
I_xI_{y} & I_{y}^{2}
\end{bmatrix}
\right )
\begin{bmatrix}
\end{bmatrix}
\right )
\begin{bmatrix}
u \\
v
v
\end{bmatrix}
* Let's denote:
......@@ -115,11 +115,11 @@ How does it work?
.. math::
M = \displaystyle \sum_{x,y}
w(x,y)
\begin{bmatrix}
I_x^{2} & I_{x}I_{y} \\
I_xI_{y} & I_{y}^{2}
\end{bmatrix}
w(x,y)
\begin{bmatrix}
I_x^{2} & I_{x}I_{y} \\
I_xI_{y} & I_{y}^{2}
\end{bmatrix}
* So, our equation now is:
......@@ -128,10 +128,10 @@ How does it work?
E(u,v) \approx \begin{bmatrix}
u & v
\end{bmatrix}
M
\begin{bmatrix}
M
\begin{bmatrix}
u \\
v
v
\end{bmatrix}
......
doc/tutorials/gpu/table_of_content_gpu/table_of_content_gpu.rst
View file @ 0e7ca71d
......@@ -7,7 +7,7 @@ Squeeze out every little computation power from your system by using the power o
.. include:: ../../definitions/tocDefinitions.rst
+
+
.. tabularcolumns:: m{100pt} m{300pt}
.. cssclass:: toctableopencv
......@@ -18,7 +18,7 @@ Squeeze out every little computation power from your system by using the power o
*Author:* |Author_BernatG|
This will give a good grasp on how to approach coding on the GPU module, once you already know how to handle the other modules. As a test case it will port the similarity methods from the tutorial :ref:`videoInputPSNRMSSIM` to the GPU.
This will give a good grasp on how to approach coding on the GPU module, once you already know how to handle the other modules. As a test case it will port the similarity methods from the tutorial :ref:`videoInputPSNRMSSIM` to the GPU.
=============== ======================================================
......
doc/tutorials/highgui/table_of_content_highgui/table_of_content_highgui.rst
View file @ 0e7ca71d
......@@ -3,30 +3,30 @@
*highgui* module. High Level GUI and Media
------------------------------------------
This section contains valuable tutorials about how to read/save your image/video files and how to use the built-in graphical user interface of the library.
This section contains valuable tutorials about how to read/save your image/video files and how to use the built-in graphical user interface of the library.
.. include:: ../../definitions/tocDefinitions.rst
+
+
.. tabularcolumns:: m{100pt} m{300pt}
.. cssclass:: toctableopencv
=============== ======================================================
|Beginners_5| *Title:* :ref:`Adding_Trackbars`
*Compatibility:* > OpenCV 2.0
*Author:* |Author_AnaH|
We will learn how to add a Trackbar to our applications
=============== ======================================================
.. |Beginners_5| image:: images/Adding_Trackbars_Tutorial_Cover.jpg
:height: 90pt
:width: 90pt
+
+
.. tabularcolumns:: m{100pt} m{300pt}
.. cssclass:: toctableopencv
......@@ -34,7 +34,7 @@ This section contains valuable tutorials about how to read/save your image/video
|hVideoInput| *Title:* :ref:`videoInputPSNRMSSIM`
*Compatibility:* > OpenCV 2.0
*Author:* |Author_BernatG|
You will learn how to read video streams, and how to calculate similarity values such as PSNR or SSIM.
......@@ -45,7 +45,7 @@ This section contains valuable tutorials about how to read/save your image/video
:height: 90pt
:width: 90pt
+
+
.. tabularcolumns:: m{100pt} m{300pt}
.. cssclass:: toctableopencv
......
doc/tutorials/highgui/trackbar/trackbar.rst
View file @ 0e7ca71d
......@@ -5,11 +5,11 @@ Adding a Trackbar to our applications!
* In the previous tutorials (about *linear blending* and the *brightness and contrast adjustments*) you might have noted that we needed to give some **input** to our programs, such as :math:`\alpha` and :math:`beta`. We accomplished that by entering this data using the Terminal
* Well, it is time to use some fancy GUI tools. OpenCV provides some GUI utilities (*highgui.h*) for you. An example of this is a **Trackbar**
* Well, it is time to use some fancy GUI tools. OpenCV provides some GUI utilities (*highgui.h*) for you. An example of this is a **Trackbar**
.. image:: images/Adding_Trackbars_Tutorial_Trackbar.png
:alt: Trackbar example
:align: center
:align: center
* In this tutorial we will just modify our two previous programs so that they get the input information from the trackbar.
......@@ -19,7 +19,7 @@ Goals
In this tutorial you will learn how to:
* Add a Trackbar in an OpenCV window by using :create_trackbar:`createTrackbar <>`
* Add a Trackbar in an OpenCV window by using :create_trackbar:`createTrackbar <>`
Code
=====
......@@ -33,13 +33,13 @@ Let's modify the program made in the tutorial :ref:`Adding_Images`. We will let
using namespace cv;
/// Global Variables
/// Global Variables
const int alpha_slider_max = 100;
int alpha_slider;
int alpha_slider;
double alpha;
double beta;
double beta;
/// Matrices to store images
/// Matrices to store images
Mat src1;
Mat src2;
Mat dst;
......@@ -49,12 +49,12 @@ Let's modify the program made in the tutorial :ref:`Adding_Images`. We will let
* @brief Callback for trackbar
*/
void on_trackbar( int, void* )
{
{
alpha = (double) alpha_slider/alpha_slider_max ;
beta = ( 1.0 - alpha );
addWeighted( src1, alpha, src2, beta, 0.0, dst);
imshow( "Linear Blend", dst );
}
......@@ -67,7 +67,7 @@ Let's modify the program made in the tutorial :ref:`Adding_Images`. We will let
if( !src1.data ) { printf("Error loading src1 \n"); return -1; }
if( !src2.data ) { printf("Error loading src2 \n"); return -1; }
/// Initialize values
/// Initialize values
alpha_slider = 0;
/// Create Windows
......@@ -75,13 +75,13 @@ Let's modify the program made in the tutorial :ref:`Adding_Images`. We will let
/// Create Trackbars
char TrackbarName[50];
sprintf( TrackbarName, "Alpha x %d", alpha_slider_max );
sprintf( TrackbarName, "Alpha x %d", alpha_slider_max );
createTrackbar( TrackbarName, "Linear Blend", &alpha_slider, alpha_slider_max, on_trackbar );
/// Show some stuff
on_trackbar( alpha_slider, 0 );
/// Wait until user press some key
waitKey(0);
return 0;
......@@ -113,7 +113,7 @@ We only analyze the code that is related to Trackbar:
createTrackbar( TrackbarName, "Linear Blend", &alpha_slider, alpha_slider_max, on_trackbar );
Note the following:
* Our Trackbar has a label **TrackbarName**
* The Trackbar is located in the window named **"Linear Blend"**
* The Trackbar values will be in the range from :math:`0` to **alpha_slider_max** (the minimum limit is always **zero**).
......@@ -125,21 +125,21 @@ We only analyze the code that is related to Trackbar:
.. code-block:: cpp
void on_trackbar( int, void* )
{
{
alpha = (double) alpha_slider/alpha_slider_max ;
beta = ( 1.0 - alpha );
addWeighted( src1, alpha, src2, beta, 0.0, dst);
imshow( "Linear Blend", dst );
}
Note that:
* We use the value of **alpha_slider** (integer) to get a double value for **alpha**.
* We use the value of **alpha_slider** (integer) to get a double value for **alpha**.
* **alpha_slider** is updated each time the trackbar is displaced by the user.
* We define *src1*, *src2*, *dist*, *alpha*, *alpha_slider* and *beta* as global variables, so they can be used everywhere.
Result
=======
......@@ -147,13 +147,13 @@ Result
.. image:: images/Adding_Trackbars_Tutorial_Result_0.jpg
:alt: Adding Trackbars - Windows Linux
:align: center
:align: center
* As a manner of practice, you can also add 02 trackbars for the program made in :ref:`Basic_Linear_Transform`. One trackbar to set :math:`\alpha` and another for :math:`\beta`. The output might look like:
.. image:: images/Adding_Trackbars_Tutorial_Result_1.jpg
:alt: Adding Trackbars - Lena
:align: center
:align: center
......
doc/tutorials/imgproc/erosion_dilatation/erosion_dilatation.rst
View file @ 0e7ca71d
......@@ -112,21 +112,21 @@ This tutorial code's is shown lines below. You can also download it from `here <
/// Create Erosion Trackbar
createTrackbar( "Element:\n 0: Rect \n 1: Cross \n 2: Ellipse", "Erosion Demo",
&erosion_elem, max_elem,
Erosion );
&erosion_elem, max_elem,
Erosion );
createTrackbar( "Kernel size:\n 2n +1", "Erosion Demo",
&erosion_size, max_kernel_size,
Erosion );
&erosion_size, max_kernel_size,
Erosion );
/// Create Dilation Trackbar
createTrackbar( "Element:\n 0: Rect \n 1: Cross \n 2: Ellipse", "Dilation Demo",
&dilation_elem, max_elem,
Dilation );
&dilation_elem, max_elem,
Dilation );
createTrackbar( "Kernel size:\n 2n +1", "Dilation Demo",
&dilation_size, max_kernel_size,
Dilation );
&dilation_size, max_kernel_size,
Dilation );
/// Default start
Erosion( 0, 0 );
......@@ -145,8 +145,8 @@ This tutorial code's is shown lines below. You can also download it from `here <
else if( erosion_elem == 2) { erosion_type = MORPH_ELLIPSE; }
Mat element = getStructuringElement( erosion_type,
Size( 2*erosion_size + 1, 2*erosion_size+1 ),
Point( erosion_size, erosion_size ) );
Size( 2*erosion_size + 1, 2*erosion_size+1 ),
Point( erosion_size, erosion_size ) );
/// Apply the erosion operation
erode( src, erosion_dst, element );
......@@ -162,8 +162,8 @@ This tutorial code's is shown lines below. You can also download it from `here <
else if( dilation_elem == 2) { dilation_type = MORPH_ELLIPSE; }
Mat element = getStructuringElement( dilation_type,
Size( 2*dilation_size + 1, 2*dilation_size+1 ),
Point( dilation_size, dilation_size ) );
Size( 2*dilation_size + 1, 2*dilation_size+1 ),
Point( dilation_size, dilation_size ) );
/// Apply the dilation operation
dilate( src, dilation_dst, element );
imshow( "Dilation Demo", dilation_dst );
......@@ -201,8 +201,8 @@ Explanation
else if( erosion_elem == 2) { erosion_type = MORPH_ELLIPSE; }
Mat element = getStructuringElement( erosion_type,
Size( 2*erosion_size + 1, 2*erosion_size+1 ),
Point( erosion_size, erosion_size ) );
Size( 2*erosion_size + 1, 2*erosion_size+1 ),
Point( erosion_size, erosion_size ) );
/// Apply the erosion operation
erode( src, erosion_dst, element );
imshow( "Erosion Demo", erosion_dst );
......@@ -216,17 +216,17 @@ Explanation
.. code-block:: cpp
Mat element = getStructuringElement( erosion_type,
Size( 2*erosion_size + 1, 2*erosion_size+1 ),
Point( erosion_size, erosion_size ) );
Mat element = getStructuringElement( erosion_type,
Size( 2*erosion_size + 1, 2*erosion_size+1 ),
Point( erosion_size, erosion_size ) );
We can choose any of three shapes for our kernel:
.. container:: enumeratevisibleitemswithsquare
+ Rectangular box: MORPH_RECT
+ Cross: MORPH_CROSS
+ Ellipse: MORPH_ELLIPSE
+ Rectangular box: MORPH_RECT
+ Cross: MORPH_CROSS
+ Ellipse: MORPH_ELLIPSE
Then, we just have to specify the size of our kernel and the *anchor point*. If not specified, it is assumed to be in the center.
......@@ -251,8 +251,8 @@ The code is below. As you can see, it is completely similar to the snippet of co
else if( dilation_elem == 2) { dilation_type = MORPH_ELLIPSE; }
Mat element = getStructuringElement( dilation_type,
Size( 2*dilation_size + 1, 2*dilation_size+1 ),
Point( dilation_size, dilation_size ) );
Size( 2*dilation_size + 1, 2*dilation_size+1 ),
Point( dilation_size, dilation_size ) );
/// Apply the dilation operation
dilate( src, dilation_dst, element );
imshow( "Dilation Demo", dilation_dst );
......
doc/tutorials/imgproc/gausian_median_blur_bilateral_filter/gausian_median_blur_bilateral_filter.rst
View file @ 0e7ca71d
......@@ -159,35 +159,35 @@ Code
if( display_caption( "Homogeneous Blur" ) != 0 ) { return 0; }
for ( int i = 1; i < MAX_KERNEL_LENGTH; i = i + 2 )
{ blur( src, dst, Size( i, i ), Point(-1,-1) );
{ blur( src, dst, Size( i, i ), Point(-1,-1) );
if( display_dst( DELAY_BLUR ) != 0 ) { return 0; } }
/// Applying Gaussian blur
if( display_caption( "Gaussian Blur" ) != 0 ) { return 0; }
for ( int i = 1; i < MAX_KERNEL_LENGTH; i = i + 2 )
{ GaussianBlur( src, dst, Size( i, i ), 0, 0 );
{ GaussianBlur( src, dst, Size( i, i ), 0, 0 );
if( display_dst( DELAY_BLUR ) != 0 ) { return 0; } }
/// Applying Median blur
if( display_caption( "Median Blur" ) != 0 ) { return 0; }
if( display_caption( "Median Blur" ) != 0 ) { return 0; }
for ( int i = 1; i < MAX_KERNEL_LENGTH; i = i + 2 )
{ medianBlur ( src, dst, i );
for ( int i = 1; i < MAX_KERNEL_LENGTH; i = i + 2 )
{ medianBlur ( src, dst, i );
if( display_dst( DELAY_BLUR ) != 0 ) { return 0; } }
/// Applying Bilateral Filter
if( display_caption( "Bilateral Blur" ) != 0 ) { return 0; }
/// Applying Bilateral Filter
if( display_caption( "Bilateral Blur" ) != 0 ) { return 0; }
for ( int i = 1; i < MAX_KERNEL_LENGTH; i = i + 2 )
{ bilateralFilter ( src, dst, i, i*2, i/2 );
for ( int i = 1; i < MAX_KERNEL_LENGTH; i = i + 2 )
{ bilateralFilter ( src, dst, i, i*2, i/2 );
if( display_dst( DELAY_BLUR ) != 0 ) { return 0; } }
/// Wait until user press a key
display_caption( "End: Press a key!" );
/// Wait until user press a key
display_caption( "End: Press a key!" );
waitKey(0);
return 0;
waitKey(0);
return 0;
}
int display_caption( char* caption )
......
doc/tutorials/imgproc/histograms/back_projection/back_projection.rst
View file @ 0e7ca71d
......@@ -94,7 +94,7 @@ Code
* Loads an image
* Convert the original to HSV format and separate only *Hue* channel to be used for the Histogram (using the OpenCV function :mix_channels:`mixChannels <>`)
* Let the user to enter the number of bins to be used in the calculation of the histogram.
* Calculate the histogram (and update it if the bins change) and the backprojection of the same image.
* Calculate the histogram (and update it if the bins change) and the backprojection of the same image.
* Display the backprojection and the histogram in windows.
* **Downloadable code**:
......
doc/tutorials/imgproc/imgtrans/remap/remap.rst
View file @ 0e7ca71d
......@@ -124,34 +124,34 @@ Code
for( int j = 0; j < src.rows; j++ )
{ for( int i = 0; i < src.cols; i++ )
{
{
switch( ind )
{
case 0:
if( i > src.cols*0.25 && i < src.cols*0.75 && j > src.rows*0.25 && j < src.rows*0.75 )
{
case 0:
if( i > src.cols*0.25 && i < src.cols*0.75 && j > src.rows*0.25 && j < src.rows*0.75 )
{
map_x.at<float>(j,i) = 2*( i - src.cols*0.25 ) + 0.5 ;
map_y.at<float>(j,i) = 2*( j - src.rows*0.25 ) + 0.5 ;
}
else
{ map_x.at<float>(j,i) = 0 ;
map_y.at<float>(j,i) = 0 ;
map_x.at<float>(j,i) = 2*( i - src.cols*0.25 ) + 0.5 ;
map_y.at<float>(j,i) = 2*( j - src.rows*0.25 ) + 0.5 ;
}
else
{ map_x.at<float>(j,i) = 0 ;
map_y.at<float>(j,i) = 0 ;
}
break;
case 1:
map_x.at<float>(j,i) = i ;
map_y.at<float>(j,i) = src.rows - j ;
break;
case 1:
map_x.at<float>(j,i) = i ;
map_y.at<float>(j,i) = src.rows - j ;
break;
case 2:
map_x.at<float>(j,i) = src.cols - i ;
map_y.at<float>(j,i) = j ;
break;
map_x.at<float>(j,i) = src.cols - i ;
map_y.at<float>(j,i) = j ;
break;
case 3:
map_x.at<float>(j,i) = src.cols - i ;
map_y.at<float>(j,i) = src.rows - j ;
break;
map_x.at<float>(j,i) = src.cols - i ;
map_y.at<float>(j,i) = src.rows - j ;
break;
} // end of switch
}
}
}
ind++;
}
......@@ -241,34 +241,34 @@ Explanation
for( int j = 0; j < src.rows; j++ )
{ for( int i = 0; i < src.cols; i++ )
{
{
switch( ind )
{
case 0:
if( i > src.cols*0.25 && i < src.cols*0.75 && j > src.rows*0.25 && j < src.rows*0.75 )
{
case 0:
if( i > src.cols*0.25 && i < src.cols*0.75 && j > src.rows*0.25 && j < src.rows*0.75 )
{
map_x.at<float>(j,i) = 2*( i - src.cols*0.25 ) + 0.5 ;
map_y.at<float>(j,i) = 2*( j - src.rows*0.25 ) + 0.5 ;
}
else
{ map_x.at<float>(j,i) = 0 ;
map_y.at<float>(j,i) = 0 ;
map_x.at<float>(j,i) = 2*( i - src.cols*0.25 ) + 0.5 ;
map_y.at<float>(j,i) = 2*( j - src.rows*0.25 ) + 0.5 ;
}
else
{ map_x.at<float>(j,i) = 0 ;
map_y.at<float>(j,i) = 0 ;
}
break;
case 1:
map_x.at<float>(j,i) = i ;
map_y.at<float>(j,i) = src.rows - j ;
break;
case 1:
map_x.at<float>(j,i) = i ;
map_y.at<float>(j,i) = src.rows - j ;
break;
case 2:
map_x.at<float>(j,i) = src.cols - i ;
map_y.at<float>(j,i) = j ;
break;
map_x.at<float>(j,i) = src.cols - i ;
map_y.at<float>(j,i) = j ;
break;
case 3:
map_x.at<float>(j,i) = src.cols - i ;
map_y.at<float>(j,i) = src.rows - j ;
break;
map_x.at<float>(j,i) = src.cols - i ;
map_y.at<float>(j,i) = src.rows - j ;
break;
} // end of switch
}
}
}
ind++;
}
......
doc/tutorials/imgproc/opening_closing_hats/opening_closing_hats.rst
View file @ 0e7ca71d
......@@ -154,13 +154,13 @@ This tutorial code's is shown lines below. You can also download it from `here <
/// Create Trackbar to select kernel type
createTrackbar( "Element:\n 0: Rect - 1: Cross - 2: Ellipse", window_name,
&morph_elem, max_elem,
Morphology_Operations );
&morph_elem, max_elem,
Morphology_Operations );
/// Create Trackbar to choose kernel size
createTrackbar( "Kernel size:\n 2n +1", window_name,
&morph_size, max_kernel_size,
Morphology_Operations );
&morph_size, max_kernel_size,
Morphology_Operations );
/// Default start
Morphology_Operations( 0, 0 );
......@@ -211,16 +211,16 @@ Explanation
.. code-block:: cpp
createTrackbar( "Element:\n 0: Rect - 1: Cross - 2: Ellipse", window_name,
&morph_elem, max_elem,
Morphology_Operations );
&morph_elem, max_elem,
Morphology_Operations );
* The final trackbar **"Kernel Size"** returns the size of the kernel to be used (**morph_size**)
.. code-block:: cpp
createTrackbar( "Kernel size:\n 2n +1", window_name,
&morph_size, max_kernel_size,
Morphology_Operations );
&morph_size, max_kernel_size,
Morphology_Operations );
* Every time we move any slider, the user's function **Morphology_Operations** will be called to effectuate a new morphology operation and it will update the output image based on the current trackbar values.
......
doc/tutorials/imgproc/pyramids/pyramids.rst
View file @ 0e7ca71d
......@@ -129,7 +129,7 @@ This tutorial code's is shown lines below. You can also download it from `here <
c = waitKey(10);
if( (char)c == 27 )
{ break; }
{ break; }
if( (char)c == 'u' )
{ pyrUp( tmp, dst, Size( tmp.cols*2, tmp.rows*2 ) );
printf( "** Zoom In: Image x 2 \n" );
......@@ -188,7 +188,7 @@ Explanation
c = waitKey(10);
if( (char)c == 27 )
{ break; }
{ break; }
if( (char)c == 'u' )
{ pyrUp( tmp, dst, Size( tmp.cols*2, tmp.rows*2 ) );
printf( "** Zoom In: Image x 2 \n" );
......
doc/tutorials/imgproc/threshold/threshold.rst
View file @ 0e7ca71d
......@@ -174,12 +174,12 @@ The tutorial code's is shown lines below. You can also download it from `here <h
/// Create Trackbar to choose type of Threshold
createTrackbar( trackbar_type,
window_name, &threshold_type,
max_type, Threshold_Demo );
window_name, &threshold_type,
max_type, Threshold_Demo );
createTrackbar( trackbar_value,
window_name, &threshold_value,
max_value, Threshold_Demo );
window_name, &threshold_value,
max_value, Threshold_Demo );
/// Call the function to initialize
Threshold_Demo( 0, 0 );
......@@ -190,7 +190,7 @@ The tutorial code's is shown lines below. You can also download it from `here <h
int c;
c = waitKey( 20 );
if( (char)c == 27 )
{ break; }
{ break; }
}
}
......@@ -245,12 +245,12 @@ Explanation
.. code-block:: cpp
createTrackbar( trackbar_type,
window_name, &threshold_type,
max_type, Threshold_Demo );
window_name, &threshold_type,
max_type, Threshold_Demo );
createTrackbar( trackbar_value,
window_name, &threshold_value,
max_value, Threshold_Demo );
window_name, &threshold_value,
max_value, Threshold_Demo );
* Wait until the user enters the threshold value, the type of thresholding (or until the program exits)
......
doc/tutorials/introduction/ios_install/ios_install.rst
View file @ 0e7ca71d
......@@ -20,7 +20,7 @@ In MacOS it can be done using the following command in Terminal:
cd ~/<my_working _directory>
git clone https://github.com/Itseez/opencv.git
Building OpenCV from Source, using CMake and Command Line
=========================================================
......@@ -28,10 +28,10 @@ Building OpenCV from Source, using CMake and Command Line
#. Make symbolic link for Xcode to let OpenCV build scripts find the compiler, header files etc.
.. code-block:: bash
cd /
sudo ln -s /Applications/Xcode.app/Contents/Developer Developer
#. Build OpenCV framework:
.. code-block:: bash
......
doc/tutorials/introduction/linux_eclipse/linux_eclipse.rst
View file @ 0e7ca71d
......@@ -11,7 +11,7 @@ Prerequisites
1. Having installed `Eclipse <http://www.eclipse.org/>`_ in your workstation (only the CDT plugin for C/C++ is needed). You can follow the following steps:
* Go to the Eclipse site
* Go to the Eclipse site
* Download `Eclipse IDE for C/C++ Developers <http://www.eclipse.org/downloads/packages/eclipse-ide-cc-developers/heliossr2>`_ . Choose the link according to your workstation.
......@@ -20,7 +20,7 @@ Prerequisites
Making a project
=================
1. Start Eclipse. Just run the executable that comes in the folder.
1. Start Eclipse. Just run the executable that comes in the folder.
#. Go to **File -> New -> C/C++ Project**
......@@ -28,13 +28,13 @@ Making a project
:alt: Eclipse Tutorial Screenshot 0
:align: center
#. Choose a name for your project (i.e. DisplayImage). An **Empty Project** should be okay for this example.
#. Choose a name for your project (i.e. DisplayImage). An **Empty Project** should be okay for this example.
.. image:: images/a1.png
:alt: Eclipse Tutorial Screenshot 1
:align: center
#. Leave everything else by default. Press **Finish**.
#. Leave everything else by default. Press **Finish**.
#. Your project (in this case DisplayImage) should appear in the **Project Navigator** (usually at the left side of your window).
......@@ -45,7 +45,7 @@ Making a project
#. Now, let's add a source file using OpenCV:
* Right click on **DisplayImage** (in the Navigator). **New -> Folder** .
* Right click on **DisplayImage** (in the Navigator). **New -> Folder** .
.. image:: images/a4.png
:alt: Eclipse Tutorial Screenshot 4
......@@ -76,9 +76,9 @@ Making a project
image = imread( argv[1], 1 );
if( argc != 2 || !image.data )
{
{
printf( "No image data \n" );
return -1;
return -1;
}
namedWindow( "Display Image", CV_WINDOW_AUTOSIZE );
......@@ -102,7 +102,7 @@ Making a project
:align: center
.. note::
If you do not know where your opencv files are, open the **Terminal** and type:
If you do not know where your opencv files are, open the **Terminal** and type:
.. code-block:: bash
......@@ -112,56 +112,56 @@ Making a project
.. code-block:: bash
-I/usr/local/include/opencv -I/usr/local/include
-I/usr/local/include/opencv -I/usr/local/include
b. Now go to **GCC C++ Linker**,there you have to fill two spaces:
First in **Library search path (-L)** you have to write the path to where the opencv libraries reside, in my case the path is:
::
/usr/local/lib
Then in **Libraries(-l)** add the OpenCV libraries that you may need. Usually just the 3 first on the list below are enough (for simple applications) . In my case, I am putting all of them since I plan to use the whole bunch:
opencv_core
opencv_imgproc
opencv_core
opencv_imgproc
opencv_highgui
opencv_ml
opencv_video
opencv_ml
opencv_video
opencv_features2d
opencv_calib3d
opencv_objdetect
opencv_calib3d
opencv_objdetect
opencv_contrib
opencv_legacy
opencv_legacy
opencv_flann
.. image:: images/a10.png
:alt: Eclipse Tutorial Screenshot 10
:align: center
:align: center
If you don't know where your libraries are (or you are just psychotic and want to make sure the path is fine), type in **Terminal**:
.. code-block:: bash
pkg-config --libs opencv
My output (in case you want to check) was:
.. code-block:: bash
-L/usr/local/lib -lopencv_core -lopencv_imgproc -lopencv_highgui -lopencv_ml -lopencv_video -lopencv_features2d -lopencv_calib3d -lopencv_objdetect -lopencv_contrib -lopencv_legacy -lopencv_flann
-L/usr/local/lib -lopencv_core -lopencv_imgproc -lopencv_highgui -lopencv_ml -lopencv_video -lopencv_features2d -lopencv_calib3d -lopencv_objdetect -lopencv_contrib -lopencv_legacy -lopencv_flann
Now you are done. Click **OK**
* Your project should be ready to be built. For this, go to **Project->Build all**
* Your project should be ready to be built. For this, go to **Project->Build all**
In the Console you should get something like
In the Console you should get something like
.. image:: images/a12.png
:alt: Eclipse Tutorial Screenshot 12
:align: center
:align: center
If you check in your folder, there should be an executable there.
......@@ -179,21 +179,21 @@ So, now we have an executable ready to run. If we were to use the Terminal, we w
Assuming that the image to use as the argument would be located in <DisplayImage_directory>/images/HappyLittleFish.png. We can still do this, but let's do it from Eclipse:
#. Go to **Run->Run Configurations**
#. Go to **Run->Run Configurations**
#. Under C/C++ Application you will see the name of your executable + Debug (if not, click over C/C++ Application a couple of times). Select the name (in this case **DisplayImage Debug**).
#. Under C/C++ Application you will see the name of your executable + Debug (if not, click over C/C++ Application a couple of times). Select the name (in this case **DisplayImage Debug**).
#. Now, in the right side of the window, choose the **Arguments** Tab. Write the path of the image file we want to open (path relative to the workspace/DisplayImage folder). Let's use **HappyLittleFish.png**:
.. image:: images/a14.png
:alt: Eclipse Tutorial Screenshot 14
:align: center
:align: center
#. Click on the **Apply** button and then in Run. An OpenCV window should pop up with the fish image (or whatever you used).
.. image:: images/a15.jpg
:alt: Eclipse Tutorial Screenshot 15
:align: center
:align: center
#. Congratulations! You are ready to have fun with OpenCV using Eclipse.
......@@ -238,7 +238,7 @@ Say you have or create a new file, *helloworld.cpp* in a directory called *foo*:
ADD_EXECUTABLE( helloworld helloworld.cxx )
TARGET_LINK_LIBRARIES( helloworld ${OpenCV_LIBS} )
#. Run: ``cmake-gui ..`` and make sure you fill in where opencv was built.
#. Run: ``cmake-gui ..`` and make sure you fill in where opencv was built.
#. Then click ``configure`` and then ``generate``. If it's OK, **quit cmake-gui**
......
doc/tutorials/introduction/linux_gcc_cmake/linux_gcc_cmake.rst
View file @ 0e7ca71d
......@@ -11,7 +11,7 @@ Using OpenCV with gcc and CMake
* The easiest way of using OpenCV in your code is to use `CMake <http://www.cmake.org/>`_. A few advantages (taken from the Wiki):
#. No need to change anything when porting between Linux and Windows
#. Can easily be combined with other tools by CMake( i.e. Qt, ITK and VTK )
#. Can easily be combined with other tools by CMake( i.e. Qt, ITK and VTK )
* If you are not familiar with CMake, checkout the `tutorial <http://www.cmake.org/cmake/help/cmake_tutorial.html>`_ on its website.
......@@ -21,7 +21,7 @@ Steps
Create a program using OpenCV
-------------------------------
Let's use a simple program such as DisplayImage.cpp shown below.
Let's use a simple program such as DisplayImage.cpp shown below.
.. code-block:: cpp
......@@ -36,9 +36,9 @@ Let's use a simple program such as DisplayImage.cpp shown below.
image = imread( argv[1], 1 );
if( argc != 2 || !image.data )
{
{
printf( "No image data \n" );
return -1;
return -1;
}
namedWindow( "Display Image", CV_WINDOW_AUTOSIZE );
......
doc/tutorials/introduction/linux_install/linux_install.rst
View file @ 0e7ca71d
......@@ -11,8 +11,8 @@ Required Packages
.. code-block:: bash
sudo apt-get install build-essential
sudo apt-get install build-essential
* CMake 2.6 or higher;
* Git;
* GTK+2.x or higher, including headers (libgtk2.0-dev);
......@@ -48,7 +48,7 @@ In Linux it can be achieved with the following command in Terminal:
cd ~/<my_working _directory>
git clone https://github.com/Itseez/opencv.git
Building OpenCV from Source Using CMake, Using the Command Line
===============================================================
......@@ -58,26 +58,26 @@ Building OpenCV from Source Using CMake, Using the Command Line
#. Enter the <cmake_binary_dir> and type
.. code-block:: bash
cmake [<some optional parameters>] <path to the OpenCV source directory>
For example
.. code-block:: bash
cd ~/opencv
mkdir release
cd release
cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local ..
#. Enter the created temporary directory (<cmake_binary_dir>) and proceed with:
.. code-block:: bash
make
sudo make install
.. note::
If the size of the created library is a critical issue (like in case of an Android build) you can use the ``install/strip`` command to get the smallest size as possible. The *stripped* version appears to be twice as small. However, we do not recommend using this unless those extra megabytes do really matter.
doc/tutorials/introduction/load_save_image/load_save_image.rst
View file @ 0e7ca71d
......@@ -5,8 +5,8 @@ Load, Modify, and Save an Image
.. note::
We assume that by now you know how to load an image using :imread:`imread <>` and to display it in a window (using :imshow:`imshow <>`). Read the :ref:`Display_Image` tutorial otherwise.
We assume that by now you know how to load an image using :imread:`imread <>` and to display it in a window (using :imshow:`imshow <>`). Read the :ref:`Display_Image` tutorial otherwise.
Goals
======
......@@ -35,9 +35,9 @@ Here it is:
{
char* imageName = argv[1];
Mat image;
Mat image;
image = imread( imageName, 1 );
if( argc != 2 || !image.data )
{
printf( " No image data \n " );
......@@ -53,7 +53,7 @@ Here it is:
namedWindow( "Gray image", CV_WINDOW_AUTOSIZE );
imshow( imageName, image );
imshow( "Gray image", gray_image );
imshow( "Gray image", gray_image );
waitKey(0);
......@@ -67,18 +67,18 @@ Explanation
* Creating a Mat object to store the image information
* Load an image using :imread:`imread <>`, located in the path given by *imageName*. Fort this example, assume you are loading a RGB image.
#. Now we are going to convert our image from RGB to Grayscale format. OpenCV has a really nice function to do this kind of transformations:
#. Now we are going to convert our image from RGB to Grayscale format. OpenCV has a really nice function to do this kind of transformations:
.. code-block:: cpp
cvtColor( image, gray_image, CV_RGB2GRAY );
As you can see, :cvt_color:`cvtColor <>` takes as arguments:
.. container:: enumeratevisibleitemswithsquare
* a source image (*image*)
* a source image (*image*)
* a destination image (*gray_image*), in which we will save the converted image.
* an additional parameter that indicates what kind of transformation will be performed. In this case we use **CV_RGB2GRAY** (self-explanatory).
......@@ -86,7 +86,7 @@ Explanation
.. code-block:: cpp
imwrite( "../../images/Gray_Image.jpg", gray_image );
imwrite( "../../images/Gray_Image.jpg", gray_image );
Which will save our *gray_image* as *Gray_Image.jpg* in the folder *images* located two levels up of my current location.
......
doc/tutorials/introduction/windows_install/windows_install.rst
View file @ 0e7ca71d
......@@ -130,7 +130,7 @@ Building the library
#. Install |TortoiseGit|_. Choose the 32 or 64 bit version according to the type of OS you work in. While installing, locate your msysgit (if it doesn't do that automatically). Follow the wizard -- the default options are OK for the most part.
#. Choose a directory in your file system, where you will download the OpenCV libraries to. I recommend creating a new one that has short path and no special charachters in it, for example :file:`D:/OpenCV`. For this tutorial I'll suggest you do so. If you use your own path and know, what you're doing -- it's OK.
#. Choose a directory in your file system, where you will download the OpenCV libraries to. I recommend creating a new one that has short path and no special charachters in it, for example :file:`D:/OpenCV`. For this tutorial I'll suggest you do so. If you use your own path and know, what you're doing -- it's OK.
a) Clone the repository to the selected directory. After clicking *Clone* button, a window will appear where you can select from what repository you want to download source files (https://github.com/Itseez/opencv.git) and to what directory (:file:`D:/OpenCV`).
......
doc/tutorials/ios/hello/hello.rst
View file @ 0e7ca71d
......@@ -19,7 +19,7 @@ Follow this step by step guide to link OpenCV to iOS.
1. Create a new XCode project.
2. Now we need to link *opencv2.framework* with Xcode. Select the project Navigator in the left hand panel and click on project name.
2. Now we need to link *opencv2.framework* with Xcode. Select the project Navigator in the left hand panel and click on project name.
3. Under the TARGETS click on Build Phases. Expand Link Binary With Libraries option.
......@@ -29,10 +29,10 @@ Follow this step by step guide to link OpenCV to iOS.
.. image:: images/linking_opencv_ios.png
:alt: OpenCV iOS in Xcode
:align: center
:align: center
*Hello OpenCV iOS Application*
===============================
===============================
Now we will learn how to write a simple Hello World Application in Xcode using OpenCV.
......@@ -43,13 +43,13 @@ Now we will learn how to write a simple Hello World Application in Xcode using O
.. code-block:: cpp
#ifdef __cplusplus
#import <opencv2/opencv.hpp>
#endif
#ifdef __cplusplus
#import <opencv2/opencv.hpp>
#endif
.. image:: images/header_directive.png
:alt: header
:align: center
:align: center
.. container:: enumeratevisibleitemswithsquare
......@@ -61,7 +61,7 @@ Now we will learn how to write a simple Hello World Application in Xcode using O
.. image:: images/view_did_load.png
:alt: view did load
:align: center
:align: center
.. container:: enumeratevisibleitemswithsquare
......@@ -73,4 +73,4 @@ Now we will learn how to write a simple Hello World Application in Xcode using O
.. image:: images/output.png
:alt: output
:align: center
doc/tutorials/ios/image_manipulation/image_manipulation.rst
View file @ 0e7ca71d
......@@ -21,9 +21,9 @@ In *OpenCV* all the image processing operations are done on *Mat*. iOS uses UIIm
CGColorSpaceRef colorSpace = CGImageGetColorSpace(image.CGImage);
CGFloat cols = image.size.width;
CGFloat rows = image.size.height;
cv::Mat cvMat(rows, cols, CV_8UC4); // 8 bits per component, 4 channels
CGContextRef contextRef = CGBitmapContextCreate(cvMat.data, // Pointer to data
cols, // Width of bitmap
rows, // Height of bitmap
......@@ -32,11 +32,11 @@ In *OpenCV* all the image processing operations are done on *Mat*. iOS uses UIIm
colorSpace, // Colorspace
kCGImageAlphaNoneSkipLast |
kCGBitmapByteOrderDefault); // Bitmap info flags
CGContextDrawImage(contextRef, CGRectMake(0, 0, cols, rows), image.CGImage);
CGContextRelease(contextRef);
CGColorSpaceRelease(colorSpace);
return cvMat;
}
......@@ -47,9 +47,9 @@ In *OpenCV* all the image processing operations are done on *Mat*. iOS uses UIIm
CGColorSpaceRef colorSpace = CGImageGetColorSpace(image.CGImage);
CGFloat cols = image.size.width;
CGFloat rows = image.size.height;
cv::Mat cvMat(rows, cols, CV_8UC1); // 8 bits per component, 1 channels
CGContextRef contextRef = CGBitmapContextCreate(cvMat.data, // Pointer to data
cols, // Width of bitmap
rows, // Height of bitmap
......@@ -58,11 +58,11 @@ In *OpenCV* all the image processing operations are done on *Mat*. iOS uses UIIm
colorSpace, // Colorspace
kCGImageAlphaNoneSkipLast |
kCGBitmapByteOrderDefault); // Bitmap info flags
CGContextDrawImage(contextRef, CGRectMake(0, 0, cols, rows), image.CGImage);
CGContextRelease(contextRef);
CGColorSpaceRelease(colorSpace);
return cvMat;
}
......@@ -81,15 +81,15 @@ After the processing we need to convert it back to UIImage.
{
NSData *data = [NSData dataWithBytes:cvMat.data length:cvMat.elemSize()*cvMat.total()];
CGColorSpaceRef colorSpace;
if (cvMat.elemSize() == 1) {
colorSpace = CGColorSpaceCreateDeviceGray();
} else {
colorSpace = CGColorSpaceCreateDeviceRGB();
}
CGDataProviderRef provider = CGDataProviderCreateWithCFData((__bridge CFDataRef)data);
// Creating CGImage from cv::Mat
CGImageRef imageRef = CGImageCreate(cvMat.cols, //width
cvMat.rows, //height
......@@ -103,15 +103,15 @@ After the processing we need to convert it back to UIImage.
false, //should interpolate
kCGRenderingIntentDefault //intent
);
// Getting UIImage from CGImage
UIImage *finalImage = [UIImage imageWithCGImage:imageRef];
CGImageRelease(imageRef);
CGDataProviderRelease(provider);
CGColorSpaceRelease(colorSpace);
return finalImage;
return finalImage;
}
*Output*
......@@ -119,9 +119,9 @@ After the processing we need to convert it back to UIImage.
.. image:: images/output.jpg
:alt: header
:align: center
:align: center
Check out an instance of running code with more Image Effects on `YouTube <http://www.youtube.com/watch?v=Ko3K_xdhJ1I>`_ .
Check out an instance of running code with more Image Effects on `YouTube <http://www.youtube.com/watch?v=Ko3K_xdhJ1I>`_ .
.. raw:: html
......
doc/tutorials/ios/table_of_content_ios/table_of_content_ios.rst
View file @ 0e7ca71d
......@@ -69,7 +69,7 @@
.. toctree::
:hidden:
../hello/hello
../image_manipulation/image_manipulation
../video_processing/video_processing
doc/tutorials/ios/video_processing/video_processing.rst
View file @ 0e7ca71d
......@@ -17,35 +17,35 @@ Including OpenCV library in your iOS project
The OpenCV library comes as a so-called framework, which you can directly drag-and-drop into your XCode project. Download the latest binary from <http://sourceforge.net/projects/opencvlibrary/files/opencv-ios/>. Alternatively follow this guide :ref:`iOS-Installation` to compile the framework manually. Once you have the framework, just drag-and-drop into XCode:
.. image:: images/xcode_hello_ios_framework_drag_and_drop.png
.. image:: images/xcode_hello_ios_framework_drag_and_drop.png
Also you have to locate the prefix header that is used for all header files in the project. The file is typically located at "ProjectName/Supporting Files/ProjectName-Prefix.pch". There, you have add an include statement to import the opencv library. However, make sure you include opencv before you include UIKit and Foundation, because else you will get some weird compile errors that some macros like min and max are defined multiple times. For example the prefix header could look like the following:
.. code-block:: objc
:linenos:
//
// Prefix header for all source files of the 'VideoFilters' target in the 'VideoFilters' project
//
#import <Availability.h>
#ifndef __IPHONE_4_0
#warning "This project uses features only available in iOS SDK 4.0 and later."
#endif
#ifdef __cplusplus
#import <opencv2/opencv.hpp>
#endif
#ifdef __OBJC__
#import <UIKit/UIKit.h>
#import <Foundation/Foundation.h>
#endif
:linenos:
//
// Prefix header for all source files of the 'VideoFilters' target in the 'VideoFilters' project
//
#import <Availability.h>
#ifndef __IPHONE_4_0
#warning "This project uses features only available in iOS SDK 4.0 and later."
#endif
#ifdef __cplusplus
#import <opencv2/opencv.hpp>
#endif
#ifdef __OBJC__
#import <UIKit/UIKit.h>
#import <Foundation/Foundation.h>
#endif
Example video frame processing project
--------------------------------------
User Interface
......@@ -53,63 +53,63 @@ User Interface
First, we create a simple iOS project, for example Single View Application. Then, we create and add an UIImageView and UIButton to start the camera and display the video frames. The storyboard could look like that:
.. image:: images/xcode_hello_ios_viewcontroller_layout.png
.. image:: images/xcode_hello_ios_viewcontroller_layout.png
Make sure to add and connect the IBOutlets and IBActions to the corresponding ViewController:
.. code-block:: objc
:linenos:
@interface ViewController : UIViewController
{
IBOutlet UIImageView* imageView;
IBOutlet UIButton* button;
}
- (IBAction)actionStart:(id)sender;
@end
:linenos:
@interface ViewController : UIViewController
{
IBOutlet UIImageView* imageView;
IBOutlet UIButton* button;
}
- (IBAction)actionStart:(id)sender;
@end
Adding the Camera
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
We add a camera controller to the view controller and initialize it when the view has loaded:
.. code-block:: objc
:linenos:
#import <opencv2/highgui/cap_ios.h>
using namespace cv;
@interface ViewController : UIViewController
{
...
CvVideoCamera* videoCamera;
}
...
@property (nonatomic, retain) CvVideoCamera* videoCamera;
@end
:linenos:
#import <opencv2/highgui/cap_ios.h>
using namespace cv;
@interface ViewController : UIViewController
{
...
CvVideoCamera* videoCamera;
}
...
@property (nonatomic, retain) CvVideoCamera* videoCamera;
@end
.. code-block:: objc
:linenos:
- (void)viewDidLoad
{
[super viewDidLoad];
// Do any additional setup after loading the view, typically from a nib.
self.videoCamera = [[CvVideoCamera alloc] initWithParentView:imageView];
self.videoCamera.defaultAVCaptureDevicePosition = AVCaptureDevicePositionFront;
self.videoCamera.defaultAVCaptureSessionPreset = AVCaptureSessionPreset352x288;
self.videoCamera.defaultAVCaptureVideoOrientation = AVCaptureVideoOrientationPortrait;
self.videoCamera.defaultFPS = 30;
self.videoCamera.grayscale = NO;
}
:linenos:
- (void)viewDidLoad
{
[super viewDidLoad];
// Do any additional setup after loading the view, typically from a nib.
self.videoCamera = [[CvVideoCamera alloc] initWithParentView:imageView];
self.videoCamera.defaultAVCaptureDevicePosition = AVCaptureDevicePositionFront;
self.videoCamera.defaultAVCaptureSessionPreset = AVCaptureSessionPreset352x288;
self.videoCamera.defaultAVCaptureVideoOrientation = AVCaptureVideoOrientationPortrait;
self.videoCamera.defaultFPS = 30;
self.videoCamera.grayscale = NO;
}
In this case, we initialize the camera and provide the imageView as a target for rendering each frame. CvVideoCamera is basically a wrapper around AVFoundation, so we provie as properties some of the AVFoundation camera options. For example we want to use the front camera, set the video size to 352x288 and a video orientation (the video camera normally outputs in landscape mode, which results in transposed data when you design a portrait application).
The property defaultFPS sets the FPS of the camera. If the processing is less fast than the desired FPS, frames are automatically dropped.
......@@ -143,7 +143,7 @@ Additionally, we have to manually add framework dependencies of the opencv frame
* Foundation
.. image:: images/xcode_hello_ios_frameworks_add_dependencies.png
.. image:: images/xcode_hello_ios_frameworks_add_dependencies.png
Processing frames
......@@ -152,35 +152,35 @@ Processing frames
We follow the delegation pattern, which is very common in iOS, to provide access to each camera frame. Basically, the View Controller has to implement the CvVideoCameraDelegate protocol and has to be set as delegate to the video camera:
.. code-block:: objc
:linenos:
@interface ViewController : UIViewController<CvVideoCameraDelegate>
:linenos:
@interface ViewController : UIViewController<CvVideoCameraDelegate>
.. code-block:: objc
:linenos:
- (void)viewDidLoad
{
...
self.videoCamera = [[CvVideoCamera alloc] initWithParentView:imageView];
self.videoCamera.delegate = self;
...
}
:linenos:
- (void)viewDidLoad
{
...
self.videoCamera = [[CvVideoCamera alloc] initWithParentView:imageView];
self.videoCamera.delegate = self;
...
}
.. code-block:: objc
:linenos:
:linenos:
#pragma mark - Protocol CvVideoCameraDelegate
#pragma mark - Protocol CvVideoCameraDelegate
#ifdef __cplusplus
- (void)processImage:(Mat&)image;
{
// Do some OpenCV stuff with the image
}
#endif
#ifdef __cplusplus
- (void)processImage:(Mat&)image;
{
// Do some OpenCV stuff with the image
}
#endif
Note that we are using C++ here (cv::Mat).
Important: You have to rename the view controller's extension .m into .mm, so that the compiler compiles it under the assumption of Objective-C++ (Objective-C and C++ mixed). Then, __cplusplus is defined when the compiler is processing the file for C++ code. Therefore, we put our code within a block where __cplusplus is defined.
......@@ -193,18 +193,18 @@ From here you can start processing video frames. For example the following snipp
.. code-block:: objc
:linenos:
- (void)processImage:(Mat&)image;
{
// Do some OpenCV stuff with the image
Mat image_copy;
cvtColor(image, image_copy, CV_BGRA2BGR);
// invert image
bitwise_not(image_copy, image_copy);
cvtColor(image_copy, image, CV_BGR2BGRA);
}
:linenos:
- (void)processImage:(Mat&)image;
{
// Do some OpenCV stuff with the image
Mat image_copy;
cvtColor(image, image_copy, CV_BGRA2BGR);
// invert image
bitwise_not(image_copy, image_copy);
cvtColor(image_copy, image, CV_BGR2BGRA);
}
Start!
......@@ -213,14 +213,14 @@ Start!
Finally, we have to tell the camera to actually start/stop working. The following code will start the camera when you press the button, assuming you connected the UI properly:
.. code-block:: objc
:linenos:
#pragma mark - UI Actions
- (IBAction)actionStart:(id)sender;
{
[self.videoCamera start];
}
:linenos:
#pragma mark - UI Actions
- (IBAction)actionStart:(id)sender;
{
[self.videoCamera start];
}
......
doc/tutorials/ml/introduction_to_svm/introduction_to_svm.rst
View file @ 0e7ca71d
......@@ -10,7 +10,7 @@ In this tutorial you will learn how to:
.. container:: enumeratevisibleitemswithsquare
+ Use the OpenCV functions :svms:`CvSVM::train <cvsvm-train>` to build a classifier based on SVMs and :svms:`CvSVM::predict <cvsvm-predict>` to test its performance.
+ Use the OpenCV functions :svms:`CvSVM::train <cvsvm-train>` to build a classifier based on SVMs and :svms:`CvSVM::predict <cvsvm-predict>` to test its performance.
What is a SVM?
==============
......@@ -36,14 +36,14 @@ Then, the operation of the SVM algorithm is based on finding the hyperplane that
.. image:: images/optimal-hyperplane.png
:alt: The Optimal hyperplane
:align: center
:align: center
How is the optimal hyperplane computed?
=======================================
Let's introduce the notation used to define formally a hyperplane:
.. math::
.. math::
f(x) = \beta_{0} + \beta^{T} x,
where :math:`\beta` is known as the *weight vector* and :math:`\beta_{0}` as the *bias*.
......@@ -106,7 +106,7 @@ Explanation
.. code-block:: cpp
Mat trainingDataMat(3, 2, CV_32FC1, trainingData);
Mat labelsMat (3, 1, CV_32FC1, labels);
Mat labelsMat (3, 1, CV_32FC1, labels);
2. **Set up SVM's parameters**
......@@ -143,7 +143,7 @@ Explanation
.. code-block:: cpp
Vec3b green(0,255,0), blue (255,0,0);
for (int i = 0; i < image.rows; ++i)
for (int j = 0; j < image.cols; ++j)
{
......@@ -152,8 +152,8 @@ Explanation
if (response == 1)
image.at<Vec3b>(j, i) = green;
else
if (response == -1)
else
if (response == -1)
image.at<Vec3b>(j, i) = blue;
}
......@@ -184,5 +184,5 @@ Results
.. image:: images/result.png
:alt: The seperated planes
:align: center
:align: center
doc/tutorials/ml/table_of_content_ml/table_of_content_ml.rst
View file @ 0e7ca71d
......@@ -5,9 +5,9 @@
Use the powerfull machine learning classes for statistical classification, regression and clustering of data.
.. include:: ../../definitions/tocDefinitions.rst
.. include:: ../../definitions/tocDefinitions.rst
+
+
.. tabularcolumns:: m{100pt} m{300pt}
.. cssclass:: toctableopencv
......@@ -18,7 +18,7 @@ Use the powerfull machine learning classes for statistical classification, regre
*Author:* |Author_FernandoI|
Learn what a Suport Vector Machine is.
Learn what a Suport Vector Machine is.
============ ==============================================
......@@ -26,7 +26,7 @@ Use the powerfull machine learning classes for statistical classification, regre
:height: 90pt
:width: 90pt
+
+
.. tabularcolumns:: m{100pt} m{300pt}
.. cssclass:: toctableopencv
......@@ -51,6 +51,6 @@ Use the powerfull machine learning classes for statistical classification, regre
.. toctree::
:hidden:
../introduction_to_svm/introduction_to_svm
../non_linear_svms/non_linear_svms
doc/tutorials/objdetect/table_of_content_objdetect/table_of_content_objdetect.rst
View file @ 0e7ca71d
......@@ -5,23 +5,23 @@
Ever wondered how your digital camera detects peoples and faces? Look here to find out!
.. include:: ../../definitions/tocDefinitions.rst
.. include:: ../../definitions/tocDefinitions.rst
+
+
.. tabularcolumns:: m{100pt} m{300pt}
.. cssclass:: toctableopencv
===================== ==============================================
|CascadeClassif| **Title:** :ref:`cascade_classifier`
*Compatibility:* > OpenCV 2.0
*Author:* |Author_AnaH|
Here we learn how to use *objdetect* to find objects in our images or videos
===================== ==============================================
.. |CascadeClassif| image:: images/Cascade_Classifier_Tutorial_Cover.jpg
:height: 90pt
:width: 90pt
......
doc/tutorials/video/table_of_content_video/table_of_content_video.rst
View file @ 0e7ca71d
......@@ -3,7 +3,7 @@
*video* module. Video analysis
-----------------------------------------------------------
Look here in order to find use on your video stream algoritms like: motion extraction, feature tracking and foreground extractions.
Look here in order to find use on your video stream algoritms like: motion extraction, feature tracking and foreground extractions.
.. include:: ../../definitions/noContent.rst
......
doc/user_guide/ug_features2d.rst
View file @ 0e7ca71d
......@@ -78,7 +78,7 @@ First, we create an instance of a keypoint detector. All detectors inherit the a
extractor.compute(img1, keypoints1, descriptors1);
extractor.compute(img2, keypoints2, descriptors2);
We create an instance of descriptor extractor. The most of OpenCV descriptors inherit ``DescriptorExtractor`` abstract interface. Then we compute descriptors for each of the keypoints. The output ``Mat`` of the ``DescriptorExtractor::compute`` method contains a descriptor in a row *i* for each *i*-th keypoint. Note that the method can modify the keypoints vector by removing the keypoints such that a descriptor for them is not defined (usually these are the keypoints near image border). The method makes sure that the ouptut keypoints and descriptors are consistent with each other (so that the number of keypoints is equal to the descriptors row count). ::
We create an instance of descriptor extractor. The most of OpenCV descriptors inherit ``DescriptorExtractor`` abstract interface. Then we compute descriptors for each of the keypoints. The output ``Mat`` of the ``DescriptorExtractor::compute`` method contains a descriptor in a row *i* for each *i*-th keypoint. Note that the method can modify the keypoints vector by removing the keypoints such that a descriptor for them is not defined (usually these are the keypoints near image border). The method makes sure that the ouptut keypoints and descriptors are consistent with each other (so that the number of keypoints is equal to the descriptors row count). ::
// matching descriptors
BruteForceMatcher<L2<float> > matcher;
......
doc/user_guide/ug_mat.rst
View file @ 0e7ca71d
......@@ -13,7 +13,7 @@ Images
Load an image from a file: ::
Mat img = imread(filename)
If you read a jpg file, a 3 channel image is created by default. If you need a grayscale image, use: ::
Mat img = imread(filename, 0);
......@@ -23,14 +23,14 @@ If you read a jpg file, a 3 channel image is created by default. If you need a g
Save an image to a file: ::
imwrite(filename, img);
.. note:: format of the file is determined by its extension.
.. note:: use ``imdecode`` and ``imencode`` to read and write image from/to memory rather than a file.
XML/YAML
--------
TBD
Basic operations with images
......@@ -85,7 +85,7 @@ Memory management and reference counting
// .. fill the array
Mat pointsMat = Mat(points).reshape(1);
As a result we get a 32FC1 matrix with 3 columns instead of 32FC3 matrix with 1 column. ``pointsMat`` uses data from ``points`` and will not deallocate the memory when destroyed. In this particular instance, however, developer has to make sure that lifetime of ``points`` is longer than of ``pointsMat``.
As a result we get a 32FC1 matrix with 3 columns instead of 32FC3 matrix with 1 column. ``pointsMat`` uses data from ``points`` and will not deallocate the memory when destroyed. In this particular instance, however, developer has to make sure that lifetime of ``points`` is longer than of ``pointsMat``.
If we need to copy the data, this is done using, for example, ``Mat::copyTo`` or ``Mat::clone``: ::
Mat img = imread("image.jpg");
......@@ -115,7 +115,7 @@ A convertion from \texttt{Mat} to C API data structures: ::
IplImage img1 = img;
CvMat m = img;
Note that there is no data copying here.
Note that there is no data copying here.
Conversion from color to grey scale: ::
......
doc/user_guide/ug_traincascade.rst
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This diff is collapsed.
modules/calib3d/doc/camera_calibration_and_3d_reconstruction.rst
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......@@ -1462,7 +1462,7 @@ Reconstructs points by triangulation.
:param points4D: 4xN array of reconstructed points in homogeneous coordinates.
The function reconstructs 3-dimensional points (in homogeneous coordinates) by using their observations with a stereo camera. Projections matrices can be obtained from :ocv:func:`stereoRectify`.
The function reconstructs 3-dimensional points (in homogeneous coordinates) by using their observations with a stereo camera. Projections matrices can be obtained from :ocv:func:`stereoRectify`.
.. seealso::
......
modules/contrib/doc/facerec/facerec_changelog.rst
View file @ 0e7ca71d
......@@ -4,19 +4,19 @@ Changelog
Release 0.05
------------
This library is now included in the official OpenCV distribution (from 2.4 on).
This library is now included in the official OpenCV distribution (from 2.4 on).
The :ocv:class`FaceRecognizer` is now an :ocv:class:`Algorithm`, which better fits into the overall
OpenCV API.
OpenCV API.
To reduce the confusion on user side and minimize my work, libfacerec and OpenCV
have been synchronized and are now based on the same interfaces and implementation.
To reduce the confusion on user side and minimize my work, libfacerec and OpenCV
have been synchronized and are now based on the same interfaces and implementation.
The library now has an extensive documentation:
* The API is explained in detail and with a lot of code examples.
* The face recognition guide I had written for Python and GNU Octave/MATLAB has been adapted to the new OpenCV C++ ``cv::FaceRecognizer``.
* The face recognition guide I had written for Python and GNU Octave/MATLAB has been adapted to the new OpenCV C++ ``cv::FaceRecognizer``.
* A tutorial for gender classification with Fisherfaces.
* A tutorial for face recognition in videos (e.g. webcam).
* A tutorial for face recognition in videos (e.g. webcam).
Release highlights
......@@ -27,8 +27,8 @@ Release highlights
Release 0.04
------------
This version is fully Windows-compatible and works with OpenCV 2.3.1. Several
bugfixes, but none influenced the recognition rate.
This version is fully Windows-compatible and works with OpenCV 2.3.1. Several
bugfixes, but none influenced the recognition rate.
Release highlights
++++++++++++++++++
......@@ -40,9 +40,9 @@ Release highlights
Release 0.03
------------
Reworked the library to provide separate implementations in cpp files, because
it's the preferred way of contributing OpenCV libraries. This means the library
is not header-only anymore. Slight API changes were done, please see the
Reworked the library to provide separate implementations in cpp files, because
it's the preferred way of contributing OpenCV libraries. This means the library
is not header-only anymore. Slight API changes were done, please see the
documentation for details.
Release highlights
......@@ -55,9 +55,9 @@ Release highlights
Release 0.02
------------
Reworked the library to provide separate implementations in cpp files, because
it's the preferred way of contributing OpenCV libraries. This means the library
is not header-only anymore. Slight API changes were done, please see the
Reworked the library to provide separate implementations in cpp files, because
it's the preferred way of contributing OpenCV libraries. This means the library
is not header-only anymore. Slight API changes were done, please see the
documentation for details.
Release highlights
......@@ -80,7 +80,7 @@ Release highlights
* Eigenfaces [TP91]_
* Fisherfaces [BHK97]_
* Local Binary Patterns Histograms [AHP04]_
* Added persistence facilities to store the models with a common API.
* Unit Tests (using `gtest <http://code.google.com/p/googletest/>`_).
* Providing a CMakeLists.txt to enable easy cross-platform building.
modules/contrib/doc/facerec/facerec_tutorial.rst
View file @ 0e7ca71d
......@@ -201,7 +201,7 @@ For the first source code example, I'll go through it with you. I am first givin
.. literalinclude:: src/facerec_eigenfaces.cpp
:language: cpp
:linenos:
The source code for this demo application is also available in the ``src`` folder coming with this documentation:
* :download:`src/facerec_eigenfaces.cpp <src/facerec_eigenfaces.cpp>`
......
modules/contrib/doc/facerec/tutorial/facerec_save_load.rst
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......@@ -6,7 +6,7 @@ Introduction
Saving and loading a :ocv:class:`FaceRecognizer` is very important. Training a FaceRecognizer can be a very time-intense task, plus it's often impossible to ship the whole face database to the user of your product. The task of saving and loading a FaceRecognizer is easy with :ocv:class:`FaceRecognizer`. You only have to call :ocv:func:`FaceRecognizer::load` for loading and :ocv:func:`FaceRecognizer::save` for saving a :ocv:class:`FaceRecognizer`.
I'll adapt the Eigenfaces example from the :doc:`../facerec_tutorial`: Imagine we want to learn the Eigenfaces of the `AT&T Facedatabase <http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html>`_, store the model to a YAML file and then load it again.
I'll adapt the Eigenfaces example from the :doc:`../facerec_tutorial`: Imagine we want to learn the Eigenfaces of the `AT&T Facedatabase <http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html>`_, store the model to a YAML file and then load it again.
From the loaded model, we'll get a prediction, show the mean, Eigenfaces and the image reconstruction.
......
modules/contrib/doc/facerec/tutorial/facerec_video_recognition.rst
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......@@ -111,7 +111,7 @@ An example. If the haar-cascade is at ``C:/opencv/data/haarcascades/haarcascade_
facerec_video.exe C:/opencv/data/haarcascades/haarcascade_frontalface_default.xml C:/facerec/data/celebrities.txt 1
That's it.
That's it.
Results
-------
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modules/core/doc/clustering.rst
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......@@ -66,8 +66,8 @@ Splits an element set into equivalency classes.
:param vec: Set of elements stored as a vector.
:param labels: Output vector of labels. It contains as many elements as ``vec``. Each label ``labels[i]`` is a 0-based cluster index of ``vec[i]`` .
:param labels: Output vector of labels. It contains as many elements as ``vec``. Each label ``labels[i]`` is a 0-based cluster index of ``vec[i]`` .
:param predicate: Equivalence predicate (pointer to a boolean function of two arguments or an instance of the class that has the method ``bool operator()(const _Tp& a, const _Tp& b)`` ). The predicate returns ``true`` when the elements are certainly in the same class, and returns ``false`` if they may or may not be in the same class.
The generic function ``partition`` implements an
......
modules/core/doc/command_line_parser.rst
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......@@ -12,30 +12,30 @@ The CommandLineParser class is designed for command line arguments parsing
.. ocv:function:: CommandLineParser::CommandLineParser(int argc, const char * const argv[], const std::string keys)
:param argc:
:param argv:
:param keys:
:param argc:
:param argv:
:param keys:
.. ocv:function:: T CommandLineParser::get<T>(const std::string& name, bool space_delete = true)
:param name:
:param space_delete:
:param name:
:param space_delete:
.. ocv:function:: T CommandLineParser::get<T>(int index, bool space_delete = true)
:param index:
:param space_delete:
:param index:
:param space_delete:
.. ocv:function:: bool CommandLineParser::has(const std::string& name)
:param name:
:param name:
.. ocv:function:: bool CommandLineParser::check()
.. ocv:function:: void CommandLineParser::about(std::string message)
:param message:
:param message:
.. ocv:function:: void CommandLineParser::printMessage()
......@@ -78,7 +78,7 @@ Syntax:
::
const std::string keys =
const std::string keys =
"{help h usage ? | | print this message }"
"{@image1 | | image1 for compare }"
"{@image2 | | image2 for compare }"
......
modules/core/doc/drawing_functions.rst
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......@@ -405,8 +405,8 @@ The number of pixels along the line is stored in ``LineIterator::count`` . The m
for(int i = 0; i < it.count; i++, ++it)
buf[i] = *(const Vec3b)*it;
// alternative way of iterating through the line
// alternative way of iterating through the line
for(int i = 0; i < it2.count; i++, ++it2)
{
Vec3b val = img.at<Vec3b>(it2.pos());
......
modules/core/doc/intro.rst
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......@@ -91,8 +91,8 @@ you can use::
Ptr<T> ptr = new T(...);
That is, ``Ptr<T> ptr`` encapsulates a pointer to a ``T`` instance and a reference counter associated with the pointer. See the
:ocv:class:`Ptr`
That is, ``Ptr<T> ptr`` encapsulates a pointer to a ``T`` instance and a reference counter associated with the pointer. See the
:ocv:class:`Ptr`
description for details.
.. _AutomaticAllocation:
......
modules/core/doc/operations_on_arrays.rst
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......@@ -2283,7 +2283,7 @@ PCA constructors
* **CV_PCA_DATA_AS_COL** indicates that the input samples are stored as matrix columns.
:param maxComponents: maximum number of components that PCA should retain; by default, all the components are retained.
:param retainedVariance: Percentage of variance that PCA should retain. Using this parameter will let the PCA decided how many components to retain but it will always keep at least 2.
The default constructor initializes an empty PCA structure. The other constructors initialize the structure and call
......@@ -2312,7 +2312,7 @@ Performs Principal Component Analysis of the supplied dataset.
* **CV_PCA_DATA_AS_COL** indicates that the input samples are stored as matrix columns.
:param maxComponents: maximum number of components that PCA should retain; by default, all the components are retained.
:param retainedVariance: Percentage of variance that PCA should retain. Using this parameter will let the PCA decided how many components to retain but it will always keep at least 2.
The operator performs PCA of the supplied dataset. It is safe to reuse the same PCA structure for multiple datasets. That is, if the structure has been previously used with another dataset, the existing internal data is reclaimed and the new ``eigenvalues``, ``eigenvectors`` , and ``mean`` are allocated and computed.
......
modules/features2d/doc/common_interfaces_of_descriptor_extractors.rst
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......@@ -41,7 +41,7 @@ Abstract base class for computing descriptors for image keypoints. ::
In this interface, a keypoint descriptor can be represented as a
dense, fixed-dimension vector of a basic type. Most descriptors
dense, fixed-dimension vector of a basic type. Most descriptors
follow this pattern as it simplifies computing
distances between descriptors. Therefore, a collection of
descriptors is represented as
......
modules/features2d/doc/object_categorization.rst
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......@@ -34,7 +34,7 @@ Lixin Fan, Jutta Willamowski, Cedric Bray, 2004. ::
BOWTrainer::add
-------------------
Adds descriptors to a training set.
Adds descriptors to a training set.
.. ocv:function:: void BOWTrainer::add( const Mat& descriptors )
......@@ -60,7 +60,7 @@ Returns the count of all descriptors stored in the training set.
BOWTrainer::cluster
-----------------------
Clusters train descriptors.
Clusters train descriptors.
.. ocv:function:: Mat BOWTrainer::cluster() const
......@@ -110,7 +110,7 @@ Class to compute an image descriptor using the *bag of visual words*. Such a com
#. Compute descriptors for a given image and its keypoints set.
#. Find the nearest visual words from the vocabulary for each keypoint descriptor.
#. Compute the bag-of-words image descriptor as is a normalized histogram of vocabulary words encountered in the image. The ``i``-th bin of the histogram is a frequency of ``i``-th word of the vocabulary in the given image.
The class declaration is the following: ::
class BOWImgDescriptorExtractor
......
modules/flann/doc/flann_clustering.rst
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......@@ -10,11 +10,11 @@ Clusters features using hierarchical k-means algorithm.
.. ocv:function:: template<typename Distance> int flann::hierarchicalClustering(const Mat& features, Mat& centers, const cvflann::KMeansIndexParams& params, Distance d = Distance())
:param features: The points to be clustered. The matrix must have elements of type ``Distance::ElementType``.
:param centers: The centers of the clusters obtained. The matrix must have type ``Distance::ResultType``. The number of rows in this matrix represents the number of clusters desired, however, because of the way the cut in the hierarchical tree is chosen, the number of clusters computed will be the highest number of the form ``(branching-1)*k+1`` that's lower than the number of clusters desired, where ``branching`` is the tree's branching factor (see description of the KMeansIndexParams).
:param centers: The centers of the clusters obtained. The matrix must have type ``Distance::ResultType``. The number of rows in this matrix represents the number of clusters desired, however, because of the way the cut in the hierarchical tree is chosen, the number of clusters computed will be the highest number of the form ``(branching-1)*k+1`` that's lower than the number of clusters desired, where ``branching`` is the tree's branching factor (see description of the KMeansIndexParams).
:param params: Parameters used in the construction of the hierarchical k-means tree.
:param d: Distance to be used for clustering.
The method clusters the given feature vectors by constructing a hierarchical k-means tree and choosing a cut in the tree that minimizes the cluster's variance. It returns the number of clusters found.
modules/gpu/doc/image_processing.rst
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......@@ -868,7 +868,7 @@ Performs pure non local means denoising without any simplification, and thus it
.. seealso::
:ocv:func:`fastNlMeansDenoising`
gpu::FastNonLocalMeansDenoising
-------------------------------
.. ocv:class:: gpu::FastNonLocalMeansDenoising
......@@ -894,19 +894,19 @@ Perform image denoising using Non-local Means Denoising algorithm http://www.ipo
:param src: Input 8-bit 1-channel, 2-channel or 3-channel image.
:param dst: Output image with the same size and type as ``src`` .
:param h: Parameter regulating filter strength. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise
:param search_window: Size in pixels of the window that is used to compute weighted average for given pixel. Should be odd. Affect performance linearly: greater search_window - greater denoising time. Recommended value 21 pixels
:param block_size: Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels
:param stream: Stream for the asynchronous invocations.
This function expected to be applied to grayscale images. For colored images look at ``FastNonLocalMeansDenoising::labMethod``.
This function expected to be applied to grayscale images. For colored images look at ``FastNonLocalMeansDenoising::labMethod``.
.. seealso::
.. seealso::
:ocv:func:`fastNlMeansDenoising`
gpu::FastNonLocalMeansDenoising::labMethod()
......@@ -920,21 +920,21 @@ Modification of ``FastNonLocalMeansDenoising::simpleMethod`` for color images
:param dst: Output image with the same size and type as ``src`` .
:param h_luminance: Parameter regulating filter strength. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise
:param float: The same as h but for color components. For most images value equals 10 will be enought to remove colored noise and do not distort colors
:param search_window: Size in pixels of the window that is used to compute weighted average for given pixel. Should be odd. Affect performance linearly: greater search_window - greater denoising time. Recommended value 21 pixels
:param block_size: Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels
:param stream: Stream for the asynchronous invocations.
The function converts image to CIELAB colorspace and then separately denoise L and AB components with given h parameters using ``FastNonLocalMeansDenoising::simpleMethod`` function.
.. seealso::
.. seealso::
:ocv:func:`fastNlMeansDenoisingColored`
gpu::alphaComp
-------------------
Composites two images using alpha opacity values contained in each image.
......
modules/gpu/doc/matrix_reductions.rst
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......@@ -185,7 +185,7 @@ Reduces a matrix to a vector.
* **CV_REDUCE_MIN** The output is the minimum (column/row-wise) of all rows/columns of the matrix.
:param dtype: When it is negative, the destination vector will have the same type as the source matrix. Otherwise, its type will be ``CV_MAKE_TYPE(CV_MAT_DEPTH(dtype), mtx.channels())`` .
The function ``reduce`` reduces the matrix to a vector by treating the matrix rows/columns as a set of 1D vectors and performing the specified operation on the vectors until a single row/column is obtained. For example, the function can be used to compute horizontal and vertical projections of a raster image. In case of ``CV_REDUCE_SUM`` and ``CV_REDUCE_AVG`` , the output may have a larger element bit-depth to preserve accuracy. And multi-channel arrays are also supported in these two reduction modes.
.. seealso:: :ocv:func:`reduce`
modules/highgui/doc/reading_and_writing_images_and_video.rst
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......@@ -20,7 +20,7 @@ Reads an image from a buffer in memory.
:param buf: Input array or vector of bytes.
:param flags: The same flags as in :ocv:func:`imread` .
:param dst: The optional output placeholder for the decoded matrix. It can save the image reallocations when the function is called repeatedly for images of the same size.
The function reads an image from the specified buffer in the memory.
......@@ -74,12 +74,12 @@ Loads an image from a file.
:param filename: Name of file to be loaded.
:param flags: Flags specifying the color type of a loaded image:
* 1 -
* CV_LOAD_IMAGE_ANYDEPTH -
* 1 -
* CV_LOAD_IMAGE_ANYDEPTH -
CV_LOAD_IMAGE_COLOR
CV_LOAD_IMAGE_GRAYSCALE
* **>0** Return a 3-channel color image.
.. note:: In the current implementation the alpha channel, if any, is stripped from the output image. Use negative value if you need the alpha channel.
......
modules/highgui/src/files_Qt/Milky/README.txt
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......@@ -16,4 +16,4 @@ The license does not permit the following uses:
You may not use, or allow anyone else to use the icons to create pornographic, libelous, obscene, or defamatory material.
All icon files are provided "as is". You agree not to hold IconEden.com liable for any damages that may occur due to use, or inability to use, icons or image data from IconEden.com.
\ No newline at end of file
All icon files are provided "as is". You agree not to hold IconEden.com liable for any damages that may occur due to use, or inability to use, icons or image data from IconEden.com.
\ No newline at end of file
modules/imgproc/doc/histograms.rst
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......@@ -174,7 +174,7 @@ Compares two histograms.
* **CV_COMP_INTERSECT** Intersection
* **CV_COMP_BHATTACHARYYA** Bhattacharyya distance
* **CV_COMP_HELLINGER** Synonym for ``CV_COMP_BHATTACHARYYA``
The functions ``compareHist`` compare two dense or two sparse histograms using the specified method:
......
modules/ml/doc/gradient_boosted_trees.rst
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modules/ml/doc/k_nearest_neighbors.rst
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......@@ -9,7 +9,7 @@ CvKNearest
----------
.. ocv:class:: CvKNearest : public CvStatModel
The class implements K-Nearest Neighbors model as described in the beginning of this section.
The class implements K-Nearest Neighbors model as described in the beginning of this section.
CvKNearest::CvKNearest
----------------------
......@@ -39,7 +39,7 @@ Trains the model.
:param updateBase: Specifies whether the model is trained from scratch (``update_base=false``), or it is updated using the new training data (``update_base=true``). In the latter case, the parameter ``maxK`` must not be larger than the original value.
The method trains the K-Nearest model. It follows the conventions of the generic :ocv:func:`CvStatModel::train` approach with the following limitations:
The method trains the K-Nearest model. It follows the conventions of the generic :ocv:func:`CvStatModel::train` approach with the following limitations:
* Only ``CV_ROW_SAMPLE`` data layout is supported.
* Input variables are all ordered.
......
modules/ml/doc/mldata.rst
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modules/objdetect/doc/cascade_classification.rst
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......@@ -177,7 +177,7 @@ Loads a classifier from a file.
CascadeClassifier::read
---------------------------
Reads a classifier from a FileStorage node.
Reads a classifier from a FileStorage node.
.. ocv:function:: bool CascadeClassifier::read(const FileNode& node)
......
modules/ocl/doc/structures_and_functions.rst
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......@@ -16,8 +16,8 @@ Returns the list of devices
.. ocv:function:: int ocl::getDevice(std::vector<Info>& oclinfo, int devicetype = CVCL_DEVICE_TYPE_GPU)
:param oclinfo: Output vector of ``ocl::Info`` structures
:param devicetype: One of ``CVCL_DEVICE_TYPE_GPU``, ``CVCL_DEVICE_TYPE_CPU`` or ``CVCL_DEVICE_TYPE_DEFAULT``.
the function must be called before any other ``cv::ocl`` functions; it initializes ocl runtime.
modules/photo/doc/denoising.rst
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modules/photo/doc/photo.rst
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......@@ -7,5 +7,5 @@ photo. Computational Photography
.. toctree::
:maxdepth: 2
inpainting
inpainting
denoising
modules/stitching/doc/camera.rst
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......@@ -9,7 +9,7 @@ detail::CameraParams
Describes camera parameters.
.. note:: Translation is assumed to be zero during the whole stitching pipeline.
.. note:: Translation is assumed to be zero during the whole stitching pipeline.
::
......
modules/stitching/doc/high_level.rst
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samples/MacOSX/FaceTracker/README.txt
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