All guidelines for contributing to the OpenCV repository can be found at [`How to contribute guideline`](https://github.com/Itseez/opencv/wiki/How_to_contribute).
All guidelines for contributing to the OpenCV repository can be found at [`How to contribute guideline`](https://github.com/opencv/opencv/wiki/How_to_contribute).
See the samples \texttt{\href{https://github.com/Itseez/opencv/tree/master/samples/cpp/watershed.cpp}{watershed.cpp}} and \texttt{\href{https://github.com/Itseez/opencv/tree/master/samples/cpp/grabcut.cpp}{grabcut.cpp}}.
See the samples \texttt{\href{https://github.com/opencv/opencv/tree/master/samples/cpp/watershed.cpp}{watershed.cpp}} and \texttt{\href{https://github.com/opencv/opencv/tree/master/samples/cpp/grabcut.cpp}{grabcut.cpp}}.
\end{tabular}
@ -465,7 +465,7 @@ Example. Compute Hue-Saturation histogram of an image:\\
\end{tabbing}
\subsection{Contours}
See \texttt{\href{https://github.com/Itseez/opencv/tree/master/samples/cpp/contours2.cpp}{contours2.cpp}} and \texttt{\href{https://github.com/Itseez/opencv/tree/master/samples/cpp/squares.cpp}{squares.cpp}}
See \texttt{\href{https://github.com/opencv/opencv/tree/master/samples/cpp/contours2.cpp}{contours2.cpp}} and \texttt{\href{https://github.com/opencv/opencv/tree/master/samples/cpp/squares.cpp}{squares.cpp}}
samples on what are the contours and how to use them.
\section{Data I/O}
@ -559,7 +559,7 @@ samples on what are the contours and how to use them.
\end{tabular}
See \texttt{\href{https://github.com/Itseez/opencv/tree/master/samples/cpp/camshiftdemo.cpp}{camshiftdemo.cpp}} and other \href{https://github.com/Itseez/opencv/tree/master/samples/}{OpenCV samples} on how to use the GUI functions.
See \texttt{\href{https://github.com/opencv/opencv/tree/master/samples/cpp/camshiftdemo.cpp}{camshiftdemo.cpp}} and other \href{https://github.com/opencv/opencv/tree/master/samples/}{OpenCV samples} on how to use the GUI functions.
\section{Camera Calibration, Pose Estimation and Depth Estimation}
@ -586,10 +586,10 @@ See \texttt{\href{https://github.com/Itseez/opencv/tree/master/samples/cpp/camsh
\end{tabular}
To calibrate a camera, you can use \texttt{\href{https://github.com/Itseez/opencv/tree/master/samples/cpp/calibration.cpp}{calibration.cpp}} or
@ -597,9 +597,9 @@ To get the disparity maps and the point clouds, use
@{}p{\linewidth-\the\MyLen}@{}}
\texttt{\href{http://docs.opencv.org/modules/imgproc/doc/object_detection.html\#matchtemplate}{matchTemplate}}& Compute proximity map for given template.\\
\texttt{\href{http://docs.opencv.org/modules/objdetect/doc/cascade_classification.html\#cascadeclassifier}{CascadeClassifier}}& Viola's Cascade of Boosted classifiers using Haar or LBP features. Suits for detecting faces, facial features and some other objects without diverse textures. See \texttt{\href{https://github.com/Itseez/opencv/tree/master/samples/c/facedetect.cpp}{facedetect.cpp}}\\
\texttt{\href{http://docs.opencv.org/modules/objdetect/doc/cascade_classification.html\#cascadeclassifier}{CascadeClassifier}}& Viola's Cascade of Boosted classifiers using Haar or LBP features. Suits for detecting faces, facial features and some other objects without diverse textures. See \texttt{\href{https://github.com/opencv/opencv/tree/master/samples/c/facedetect.cpp}{facedetect.cpp}}\\
\texttt{{HOGDescriptor}}& N. Dalal's object detector using Histogram-of-Oriented-Gradients (HOG) features. Suits for detecting people, cars and other objects with well-defined silhouettes. See \texttt{\href{https://github.com/Itseez/opencv/tree/master/samples/cpp/peopledetect.cpp}{peopledetect.cpp}}\\
\texttt{{HOGDescriptor}}& N. Dalal's object detector using Histogram-of-Oriented-Gradients (HOG) features. Suits for detecting people, cars and other objects with well-defined silhouettes. See \texttt{\href{https://github.com/opencv/opencv/tree/master/samples/cpp/peopledetect.cpp}{peopledetect.cpp}}\\
* This code is in your OpenCV sample folder. Otherwise you can grab it from `here <https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/core/Matrix/Drawing_1.cpp>`_
* This code is in your OpenCV sample folder. Otherwise you can grab it from `here <https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/core/Matrix/Drawing_1.cpp>`_
This tutorial code's is shown lines below. You can also download it from `here <https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/features2D/SURF_descriptor.cpp>`_
This tutorial code's is shown lines below. You can also download it from `here <https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/features2D/SURF_descriptor.cpp>`_
This tutorial code's is shown lines below. You can also download it from `here <https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/features2D/SURF_FlannMatcher.cpp>`_
This tutorial code's is shown lines below. You can also download it from `here <https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/features2D/SURF_FlannMatcher.cpp>`_
This tutorial code's is shown lines below. You can also download it from `here <https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/TrackingMotion/cornerSubPix_Demo.cpp>`_
This tutorial code's is shown lines below. You can also download it from `here <https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/TrackingMotion/cornerSubPix_Demo.cpp>`_
This tutorial code's is shown lines below. You can also download it from `here <https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/TrackingMotion/cornerDetector_Demo.cpp>`_
This tutorial code's is shown lines below. You can also download it from `here <https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/TrackingMotion/cornerDetector_Demo.cpp>`_
This tutorial code's is shown lines below. You can also download it from `here <https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/TrackingMotion/goodFeaturesToTrack_Demo.cpp>`_
This tutorial code's is shown lines below. You can also download it from `here <https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/TrackingMotion/goodFeaturesToTrack_Demo.cpp>`_
This tutorial code's is shown lines below. You can also download it from `here <https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/TrackingMotion/cornerHarris_Demo.cpp>`_
This tutorial code's is shown lines below. You can also download it from `here <https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/TrackingMotion/cornerHarris_Demo.cpp>`_
This tutorial code's is shown lines below. You can also download it from `here <https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/ImgProc/Morphology_1.cpp>`_
This tutorial code's is shown lines below. You can also download it from `here <https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/ImgProc/Morphology_1.cpp>`_
a. Click `here <https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/Histograms_Matching/calcBackProject_Demo1.cpp>`_ for the basic version (explained in this tutorial).
b. For stuff slightly fancier (using H-S histograms and floodFill to define a mask for the skin area) you can check the `improved demo <https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/Histograms_Matching/calcBackProject_Demo2.cpp>`_
c. ...or you can always check out the classical `camshiftdemo <https://github.com/Itseez/opencv/tree/master/samples/cpp/camshiftdemo.cpp>`_ in samples.
a. Click `here <https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/Histograms_Matching/calcBackProject_Demo1.cpp>`_ for the basic version (explained in this tutorial).
b. For stuff slightly fancier (using H-S histograms and floodFill to define a mask for the skin area) you can check the `improved demo <https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/Histograms_Matching/calcBackProject_Demo2.cpp>`_
c. ...or you can always check out the classical `camshiftdemo <https://github.com/opencv/opencv/tree/master/samples/cpp/camshiftdemo.cpp>`_ in samples.
* Applies the *Canny Detector* and generates a **mask** (bright lines representing the edges on a black background).
* Applies the mask obtained on the original image and display it in a window.
#. The tutorial code's is shown lines below. You can also download it from `here <https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/ImgTrans/CannyDetector_Demo.cpp>`_
#. The tutorial code's is shown lines below. You can also download it from `here <https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/ImgTrans/CannyDetector_Demo.cpp>`_
The user chooses either option by pressing 'c' (constant) or 'r' (replicate)
* The program finishes when the user presses 'ESC'
#. The tutorial code's is shown lines below. You can also download it from `here <https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/ImgTrans/copyMakeBorder_demo.cpp>`_
#. The tutorial code's is shown lines below. You can also download it from `here <https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/ImgTrans/copyMakeBorder_demo.cpp>`_
* The filter output (with each kernel) will be shown during 500 milliseconds
#. The tutorial code's is shown lines below. You can also download it from `here <https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/ImgTrans/filter2D_demo.cpp>`_
#. The tutorial code's is shown lines below. You can also download it from `here <https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/ImgTrans/filter2D_demo.cpp>`_
#. The sample code that we will explain can be downloaded from |TutorialHoughCirclesSimpleDownload|_. A slightly fancier version (which shows both Hough standard and probabilistic with trackbars for changing the threshold values) can be found |TutorialHoughCirclesFancyDownload|_.
* Applies a Laplacian operator to the grayscale image and stores the output image
* Display the result in a window
#. The tutorial code's is shown lines below. You can also download it from `here <https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/ImgTrans/Laplace_Demo.cpp>`_
#. The tutorial code's is shown lines below. You can also download it from `here <https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/ImgTrans/Laplace_Demo.cpp>`_
* Each second, apply 1 of 4 different remapping processes to the image and display them indefinitely in a window.
* Wait for the user to exit the program
#. The tutorial code's is shown lines below. You can also download it from `here <https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/ImgTrans/Remap_Demo.cpp>`_
#. The tutorial code's is shown lines below. You can also download it from `here <https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/ImgTrans/Remap_Demo.cpp>`_
* Applies the *Sobel Operator* and generates as output an image with the detected *edges* bright on a darker background.
#. The tutorial code's is shown lines below. You can also download it from `here <https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/ImgTrans/Sobel_Demo.cpp>`_
#. The tutorial code's is shown lines below. You can also download it from `here <https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/ImgTrans/Sobel_Demo.cpp>`_
* Applies a Rotation to the image after being transformed. This rotation is with respect to the image center
* Waits until the user exits the program
#. The tutorial code's is shown lines below. You can also download it from `here <https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/ImgTrans/Geometric_Transforms_Demo.cpp>`_
#. The tutorial code's is shown lines below. You can also download it from `here <https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/ImgTrans/Geometric_Transforms_Demo.cpp>`_
This tutorial code's is shown lines below. You can also download it from `here <https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/ImgProc/Morphology_2.cpp>`_
This tutorial code's is shown lines below. You can also download it from `here <https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/ImgProc/Morphology_2.cpp>`_
This tutorial code's is shown lines below. You can also download it from `here <https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/ImgProc/Pyramids.cpp>`_
This tutorial code's is shown lines below. You can also download it from `here <https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/ImgProc/Pyramids.cpp>`_
This tutorial code's is shown lines below. You can also download it from `here <https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/ShapeDescriptors/generalContours_demo1.cpp>`_
This tutorial code's is shown lines below. You can also download it from `here <https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/ShapeDescriptors/generalContours_demo1.cpp>`_
This tutorial code's is shown lines below. You can also download it from `here <https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/ShapeDescriptors/generalContours_demo2.cpp>`_
This tutorial code's is shown lines below. You can also download it from `here <https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/ShapeDescriptors/generalContours_demo2.cpp>`_
This tutorial code's is shown lines below. You can also download it from `here <https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/ShapeDescriptors/findContours_demo.cpp>`_
This tutorial code's is shown lines below. You can also download it from `here <https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/ShapeDescriptors/findContours_demo.cpp>`_
This tutorial code's is shown lines below. You can also download it from `here <https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/ShapeDescriptors/hull_demo.cpp>`_
This tutorial code's is shown lines below. You can also download it from `here <https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/ShapeDescriptors/hull_demo.cpp>`_
This tutorial code's is shown lines below. You can also download it from `here <https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/ShapeDescriptors/moments_demo.cpp>`_
This tutorial code's is shown lines below. You can also download it from `here <https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/ShapeDescriptors/moments_demo.cpp>`_
This tutorial code's is shown lines below. You can also download it from `here <https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/ShapeDescriptors/pointPolygonTest_demo.cpp>`_
This tutorial code's is shown lines below. You can also download it from `here <https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/ShapeDescriptors/pointPolygonTest_demo.cpp>`_
The tutorial code's is shown lines below. You can also download it from `here <https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/ImgProc/Threshold.cpp>`_
The tutorial code's is shown lines below. You can also download it from `here <https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/ImgProc/Threshold.cpp>`_
@ -17,7 +17,7 @@ In this tutorial you will learn how to:
Source Code
===========
Download the source code from `here <https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/introduction/display_image/display_image.cpp>`_.
Download the source code from `here <https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/introduction/display_image/display_image.cpp>`_.
@ -64,7 +64,7 @@ In our world, achieving some tasks is possible in multiple ways. However, some o
Now the best option would be to do the integration yourself. For this, you first need to have the source code. I recommend following the guides for your operating system on acquiring OpenCV sources. For Linux users look :ref:`here <Linux-Installation>` and for :ref:`Windows here <Windows_Installation>`. You must also install python and sphinx with its dependencies in order to be able to build the documentation.
Once you have downloaded the repository to your hard drive you can take a look in the OpenCV directory to make sure you have both the samples and doc folder present. Anyone may download the latest source files from :file:`git://github.com/Itseez/opencv.git`. Nevertheless, not everyone has upload (commit/submit) rights. This is to protect the integrity of the library. If you plan doing more than one tutorial, and would like to have an account with commit user rights you should first register an account at http://code.opencv.org/ and then contact OpenCV administrator -delete-admin@-delete-opencv.org. Otherwise, you can just send the resulting files to us at -delete-admin@-delete-opencv.org and we'll add it.
Once you have downloaded the repository to your hard drive you can take a look in the OpenCV directory to make sure you have both the samples and doc folder present. Anyone may download the latest source files from :file:`git://github.com/opencv/opencv.git`. Nevertheless, not everyone has upload (commit/submit) rights. This is to protect the integrity of the library. If you plan doing more than one tutorial, and would like to have an account with commit user rights you should first register an account at http://code.opencv.org/ and then contact OpenCV administrator -delete-admin@-delete-opencv.org. Otherwise, you can just send the resulting files to us at -delete-admin@-delete-opencv.org and we'll add it.
@ -29,7 +29,7 @@ The packages can be installed using a terminal and the following commands or by
Getting OpenCV Source Code
==========================
You can use the latest stable OpenCV version available in *sourceforge* or you can grab the latest snapshot from our `Git repository <https://github.com/Itseez/opencv.git>`_.
You can use the latest stable OpenCV version available in *sourceforge* or you can grab the latest snapshot from our `Git repository <https://github.com/opencv/opencv.git>`_.
Getting the Latest Stable OpenCV Version
----------------------------------------
@ -42,14 +42,14 @@ Getting the Latest Stable OpenCV Version
Getting the Cutting-edge OpenCV from the Git Repository
@ -46,7 +46,7 @@ You may find the content of this tutorial also inside the following videos: `Par
..warning:: These videos above are long-obsolete and contain inaccurate information. Be careful, since solutions described in those videos are no longer supported and may even break your install.
If you are building your own libraries you can take the source files from our `Git repository <https://github.com/Itseez/opencv.git>`_.
If you are building your own libraries you can take the source files from our `Git repository <https://github.com/opencv/opencv.git>`_.
Building the OpenCV library from scratch requires a couple of tools installed beforehand:
@ -128,7 +128,7 @@ Building the library
#. 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`).
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/opencv/opencv.git) and to what directory (:file:`D:/OpenCV`).
#) Push the OK button and be patient as the repository is quite a heavy download. It will take some time depending on your Internet connection.
This tutorial code's is shown lines below. You can also download it from `here <https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/objectDetection/objectDetection.cpp>`_ . The second version (using LBP for face detection) can be `found here <https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/objectDetection/objectDetection2.cpp>`_
This tutorial code's is shown lines below. You can also download it from `here <https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/objectDetection/objectDetection.cpp>`_ . The second version (using LBP for face detection) can be `found here <https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/objectDetection/objectDetection2.cpp>`_
@ -2898,5 +2898,5 @@ The above methods are usually enough for users. If you want to make your own alg
* Make a class and specify ``Algorithm`` as its base class.
* The algorithm parameters should be the class members. See ``Algorithm::get()`` for the list of possible types of the parameters.
* Add public virtual method ``AlgorithmInfo* info() const;`` to your class.
* Add constructor function, ``AlgorithmInfo`` instance and implement the ``info()`` method. The simplest way is to take https://github.com/Itseez/opencv/tree/master/modules/ml/src/ml_init.cpp as the reference and modify it according to the list of your parameters.
* Add constructor function, ``AlgorithmInfo`` instance and implement the ``info()`` method. The simplest way is to take https://github.com/opencv/opencv/tree/master/modules/ml/src/ml_init.cpp as the reference and modify it according to the list of your parameters.
* Add some public function (e.g. ``initModule_<mymodule>()``) that calls info() of your algorithm and put it into the same source file as ``info()`` implementation. This is to force C++ linker to include this object file into the target application. See ``Algorithm::create()`` for details.
@ -319,7 +319,7 @@ The methods/functions grab the next frame from video file or camera and return t
The primary use of the function is in multi-camera environments, especially when the cameras do not have hardware synchronization. That is, you call ``VideoCapture::grab()`` for each camera and after that call the slower method ``VideoCapture::retrieve()`` to decode and get frame from each camera. This way the overhead on demosaicing or motion jpeg decompression etc. is eliminated and the retrieved frames from different cameras will be closer in time.
Also, when a connected camera is multi-head (for example, a stereo camera or a Kinect device), the correct way of retrieving data from it is to call `VideoCapture::grab` first and then call :ocv:func:`VideoCapture::retrieve` one or more times with different values of the ``channel`` parameter. See https://github.com/Itseez/opencv/tree/master/samples/cpp/openni_capture.cpp
Also, when a connected camera is multi-head (for example, a stereo camera or a Kinect device), the correct way of retrieving data from it is to call `VideoCapture::grab` first and then call :ocv:func:`VideoCapture::retrieve` one or more times with different values of the ``channel`` parameter. See https://github.com/opencv/opencv/tree/master/samples/cpp/openni_capture.cpp
@ -224,7 +224,7 @@ Sets mouse handler for the specified window
:param winname:Window name
:param onMouse:Mouse callback. See OpenCV samples, such as https://github.com/Itseez/opencv/tree/master/samples/cpp/ffilldemo.cpp, on how to specify and use the callback.
:param onMouse:Mouse callback. See OpenCV samples, such as https://github.com/opencv/opencv/tree/master/samples/cpp/ffilldemo.cpp, on how to specify and use the callback.
:param userdata:The optional parameter passed to the callback.
@ -296,7 +296,7 @@ Approximates a polygonal curve(s) with the specified precision.
The functions ``approxPolyDP`` approximate a curve or a polygon with another curve/polygon with less vertices so that the distance between them is less or equal to the specified precision. It uses the Douglas-Peucker algorithm
@ -21,7 +21,7 @@ The word "cascade" in the classifier name means that the resultant classifier co
The feature used in a particular classifier is specified by its shape (1a, 2b etc.), position within the region of interest and the scale (this scale is not the same as the scale used at the detection stage, though these two scales are multiplied). For example, in the case of the third line feature (2c) the response is calculated as the difference between the sum of image pixels under the rectangle covering the whole feature (including the two white stripes and the black stripe in the middle) and the sum of the image pixels under the black stripe multiplied by 3 in order to compensate for the differences in the size of areas. The sums of pixel values over a rectangular regions are calculated rapidly using integral images (see below and the :ocv:func:`integral` description).
To see the object detector at work, have a look at the facedetect demo:
The following reference is for the detection part only. There is a separate application called ``opencv_traincascade`` that can train a cascade of boosted classifiers from a set of samples.
Alexander Alekhin
9 years ago