Mergin itseez

doc/tutorials/bioinspired/retina_model/retina_model.rst

@@ -107,14 +107,16 @@ This retina filter code includes the research contributions of phd/research coll
 Code tutorial
 =============
 
-Please refer to the original tutorial source code in file *opencv_folder/samples/cpp/tutorial_code/contrib/retina_tutorial.cpp*.
+Please refer to the original tutorial source code in file *opencv_folder/samples/cpp/tutorial_code/bioinspired/retina_tutorial.cpp*.
 
-To compile it, assuming OpenCV is correctly installed, use the following command. It requires the opencv_core *(cv::Mat and friends objects management)*, opencv_highgui *(display and image/video read)* and opencv_contrib *(Retina description)* libraries to compile.
+**Note :** do not forget that the retina model is included in the following namespace : *cv::bioinspired*.
+
+To compile it, assuming OpenCV is correctly installed, use the following command. It requires the opencv_core *(cv::Mat and friends objects management)*, opencv_highgui *(display and image/video read)* and opencv_bioinspired *(Retina description)* libraries to compile.
 
 .. code-block:: cpp
 
    // compile
-   gcc retina_tutorial.cpp -o Retina_tuto -lopencv_core -lopencv_highgui -lopencv_contrib
+   gcc retina_tutorial.cpp -o Retina_tuto -lopencv_core -lopencv_highgui -lopencv_bioinspired
 
    // Run commands : add 'log' as a last parameter to apply a spatial log sampling (simulates retina sampling)
    // run on webcam
@@ -128,7 +130,7 @@ To compile it, assuming OpenCV is correctly installed, use the following command
 
 Here is a code explanation :
 
-Retina definition is present in the contrib package and a simple include allows to use it
+Retina definition is present in the bioinspired package and a simple include allows to use it. You can rather use the specific header : *opencv2/bioinspired.hpp* if you prefer but then include the other required openv modules : *opencv2/core.hpp* and *opencv2/highgui.hpp*
 
 .. code-block:: cpp
 
@@ -229,20 +231,20 @@ Once all input parameters are processed, a first image should have been loaded,
         return -1;
     }
 
-Now, everything is ready to run the retina model. I propose here to allocate a retina instance and to manage the eventual log sampling option. The Retina constructor expects at least a cv::Size object that shows the input data size that will have to be managed. One can activate other options such as color and its related color multiplexing strategy (here Bayer multiplexing is chosen using enum cv::RETINA_COLOR_BAYER). If using log sampling, the image reduction factor (smaller output images) and log sampling strengh can be adjusted.
+Now, everything is ready to run the retina model. I propose here to allocate a retina instance and to manage the eventual log sampling option. The Retina constructor expects at least a cv::Size object that shows the input data size that will have to be managed. One can activate other options such as color and its related color multiplexing strategy (here Bayer multiplexing is chosen using *enum cv::bioinspired::RETINA_COLOR_BAYER*). If using log sampling, the image reduction factor (smaller output images) and log sampling strengh can be adjusted.
 
 .. code-block:: cpp
 
     // pointer to a retina object
-    cv::Ptr<cv::Retina> myRetina;
+    cv::Ptr<cv::bioinspired::Retina> myRetina;
 
     // if the last parameter is 'log', then activate log sampling (favour foveal vision and subsamples peripheral vision)
     if (useLogSampling)
     {
-        myRetina = cv::createRetina(inputFrame.size(), true, cv::RETINA_COLOR_BAYER, true, 2.0, 10.0);
+        myRetina = cv::bioinspired::createRetina(inputFrame.size(), true, cv::bioinspired::RETINA_COLOR_BAYER, true, 2.0, 10.0);
     }
     else// -> else allocate "classical" retina :
-        myRetina = cv::createRetina(inputFrame.size());
+        myRetina = cv::bioinspired::createRetina(inputFrame.size());
 
 Once done, the proposed code writes a default xml file that contains the default parameters of the retina. This is useful to make your own config using this template. Here generated template xml file is called *RetinaDefaultParameters.xml*.
 

doc/tutorials/bioinspired/retina_model/retina_model.rst~

@@ -0,0 +1,418 @@
+.. _Retina_Model:
+
+Discovering the human retina and its use for image processing
+*************************************************************
+
+Goal
+=====
+
+I present here a model of human retina that shows some interesting properties for image preprocessing and enhancement.
+In this tutorial you will learn how to:
+
+.. container:: enumeratevisibleitemswithsquare
+
+   + discover the main two channels outing from your retina
+
+   + see the basics to use the retina model
+
+   + discover some parameters tweaks
+
+
+General overview
+================
+
+The proposed model originates from Jeanny Herault's research [herault2010]_ at `Gipsa <http://www.gipsa-lab.inpg.fr>`_. It is involved in image processing applications with `Listic <http://www.listic.univ-savoie.fr>`_ (code maintainer and user) lab. This is not a complete model but it already present interesting properties that can be involved for enhanced image processing experience. The model allows the following human retina properties to be used :
+
+* spectral whitening that has 3 important effects: high spatio-temporal frequency signals canceling (noise), mid-frequencies details enhancement and low frequencies luminance energy reduction. This *all in one* property directly allows visual signals cleaning of classical undesired distortions introduced by image sensors and input luminance range.
+
+* local logarithmic luminance compression allows details to be enhanced even in low light conditions.
+
+* decorrelation of the details information (Parvocellular output channel) and transient information (events, motion made available at the Magnocellular output channel).
+
+The first two points are illustrated below :
+
+In the figure below, the OpenEXR image sample *CrissyField.exr*, a High Dynamic Range image is shown. In order to make it visible on this web-page, the original input image is linearly rescaled to the classical image luminance range [0-255] and is converted to 8bit/channel format. Such strong conversion hides many details because of too strong local contrasts. Furthermore, noise energy is also strong and pollutes visual information.
+
+.. image:: images/retina_TreeHdr_small.jpg
+   :alt: A High dynamic range image linearly rescaled within range [0-255].
+   :align: center
+
+In the following image, applying the ideas proposed in [benoit2010]_, as your retina does, local luminance adaptation, spatial noise removal and spectral whitening work together and transmit accurate information on lower range 8bit data channels. On this picture, noise in significantly removed, local details hidden by strong luminance contrasts are enhanced. Output image keeps its naturalness and visual content is enhanced. Color processing is based on the color multiplexing/demultiplexing method proposed in [chaix2007]_.
+
+.. image:: images/retina_TreeHdr_retina.jpg
+   :alt: A High dynamic range image compressed within range [0-255] using the retina.
+   :align: center
+
+
+*Note :* image sample can be downloaded from the `OpenEXR website <http://www.openexr.com>`_. Regarding this demonstration, before retina processing, input image has been linearly rescaled within 0-255 keeping its channels float format. 5% of its histogram ends has been cut (mostly removes wrong HDR pixels). Check out the sample *opencv/samples/cpp/OpenEXRimages_HighDynamicRange_Retina_toneMapping.cpp* for similar processing. The following demonstration will only consider classical 8bit/channel images.
+
+The retina model output channels
+================================
+
+The retina model presents two outputs that benefit from the above cited behaviors.
+
+* The first one is called the Parvocellular channel. It is mainly active in the foveal retina area (high resolution central vision with color sensitive photo-receptors), its aim is to provide accurate color vision for visual details remaining static on the retina. On the other hand objects moving on the retina projection are blurred.
+
+* The second well known channel is the Magnocellular channel. It is mainly active in the retina peripheral vision and send signals related to change events (motion, transient events, etc.). These outing signals also help visual system to focus/center retina on 'transient'/moving areas for more detailed analysis thus improving visual scene context and object classification.
+
+**NOTE :** regarding the proposed model, contrary to the real retina, we apply these two channels on the entire input images using the same resolution. This allows enhanced visual details and motion information to be extracted on all the considered images... but remember, that these two channels are complementary. For example, if Magnocellular channel gives strong energy in an area, then, the Parvocellular channel is certainly blurred there since there is a transient event.
+
+As an illustration, we apply in the following the retina model on a webcam video stream of a dark visual scene. In this visual scene, captured in an amphitheater of the university, some students are moving while talking to the teacher.
+
+In this video sequence, because of the dark ambiance, signal to noise ratio is low and color artifacts are present on visual features edges because of the low quality image capture tool-chain.
+
+.. image:: images/studentsSample_input.jpg
+   :alt: an input video stream extract sample
+   :align: center
+
+Below is shown the retina foveal vision applied on the entire image. In the used retina configuration, global luminance is preserved and local contrasts are enhanced. Also, signal to noise ratio is improved : since high frequency spatio-temporal noise is reduced, enhanced details are not corrupted by any enhanced noise.
+
+.. image:: images/studentsSample_parvo.jpg
+   :alt: the retina Parvocellular output. Enhanced details, luminance adaptation and noise removal. A processing tool for image analysis.
+   :align: center
+
+Below is the output of the Magnocellular output of the retina model. Its signals are strong where transient events occur. Here, a student is moving at the bottom of the image thus generating high energy. The remaining of the image is static however, it is corrupted by a strong noise. Here, the retina filters out most of the noise thus generating low false motion area 'alarms'. This channel can be used as a transient/moving areas detector : it would provide relevant information for a low cost segmentation tool that would highlight areas in which an event is occurring.
+
+.. image:: images/studentsSample_magno.jpg
+   :alt: the retina Magnocellular output. Enhanced transient signals (motion, etc.). A preprocessing tool for event detection.
+   :align: center
+
+Retina use case
+===============
+
+This model can be used basically for spatio-temporal video effects but also in the aim of :
+
+* performing texture analysis with enhanced signal to noise ratio and enhanced details robust against input images luminance ranges (check out the Parvocellular retina channel output)
+
+* performing motion analysis also taking benefit of the previously cited properties.
+
+Literature
+==========
+For more information, refer to the following papers :
+
+.. [benoit2010] Benoit A., Caplier A., Durette B., Herault, J., "Using Human Visual System Modeling For Bio-Inspired Low Level Image Processing", Elsevier, Computer Vision and Image Understanding 114 (2010), pp. 758-773. DOI <http://dx.doi.org/10.1016/j.cviu.2010.01.011>
+
+* Please have a look at the reference work of Jeanny Herault that you can read in his book :
+
+.. [herault2010] Vision: Images, Signals and Neural Networks: Models of Neural Processing in Visual Perception (Progress in Neural Processing),By: Jeanny Herault, ISBN: 9814273686. WAPI (Tower ID): 113266891.
+
+This retina filter code includes the research contributions of phd/research collegues from which code has been redrawn by the author :
+
+* take a look at the *retinacolor.hpp* module to discover Brice Chaix de Lavarene phD color mosaicing/demosaicing and his reference paper:
+
+.. [chaix2007] B. Chaix de Lavarene, D. Alleysson, B. Durette, J. Herault (2007). "Efficient demosaicing through recursive filtering", IEEE International Conference on Image Processing ICIP 2007
+
+* take a look at *imagelogpolprojection.hpp* to discover retina spatial log sampling which originates from Barthelemy Durette phd with Jeanny Herault. A Retina / V1 cortex projection is also proposed and originates from Jeanny's discussions. More informations in the above cited Jeanny Heraults's book.
+
+Code tutorial
+=============
+
+Please refer to the original tutorial source code in file *opencv_folder/samples/cpp/tutorial_code/bioinspired/retina_tutorial.cpp*.
+
+**Note :** do not forget that the retina model is included in the following namespace : *cv::bioinspired*.
+
+To compile it, assuming OpenCV is correctly installed, use the following command. It requires the opencv_core *(cv::Mat and friends objects management)*, opencv_highgui *(display and image/video read)* and opencv_bioinspired *(Retina description)* libraries to compile.
+
+.. code-block:: cpp
+
+   // compile
+   gcc retina_tutorial.cpp -o Retina_tuto -lopencv_core -lopencv_highgui -lopencv_bioinspired
+
+   // Run commands : add 'log' as a last parameter to apply a spatial log sampling (simulates retina sampling)
+   // run on webcam
+   ./Retina_tuto -video
+   // run on video file
+   ./Retina_tuto -video myVideo.avi
+   // run on an image
+   ./Retina_tuto -image myPicture.jpg
+   // run on an image with log sampling
+   ./Retina_tuto -image myPicture.jpg log
+
+Here is a code explanation :
+
+Retina definition is present in the bioinspired package and a simple include allows to use it. You can rather use the specific header : *opencv2/bioinspired.hpp* if you prefer but then include the other required openv modules : *opencv2/core.hpp* and *opencv2/highgui.hpp* 
+
+.. code-block:: cpp
+
+   #include "opencv2/opencv.hpp"
+
+Provide user some hints to run the program with a help function
+
+.. code-block:: cpp
+
+   // the help procedure
+   static void help(std::string errorMessage)
+   {
+    std::cout<<"Program init error : "<<errorMessage<<std::endl;
+    std::cout<<"\nProgram call procedure : retinaDemo [processing mode] [Optional : media target] [Optional LAST parameter: \"log\" to activate retina log sampling]"<<std::endl;
+    std::cout<<"\t[processing mode] :"<<std::endl;
+    std::cout<<"\t -image : for still image processing"<<std::endl;
+    std::cout<<"\t -video : for video stream processing"<<std::endl;
+    std::cout<<"\t[Optional : media target] :"<<std::endl;
+    std::cout<<"\t if processing an image or video file, then, specify the path and filename of the target to process"<<std::endl;
+    std::cout<<"\t leave empty if processing video stream coming from a connected video device"<<std::endl;
+    std::cout<<"\t[Optional : activate retina log sampling] : an optional last parameter can be specified for retina spatial log sampling"<<std::endl;
+    std::cout<<"\t set \"log\" without quotes to activate this sampling, output frame size will be divided by 4"<<std::endl;
+    std::cout<<"\nExamples:"<<std::endl;
+    std::cout<<"\t-Image processing : ./retinaDemo -image lena.jpg"<<std::endl;
+    std::cout<<"\t-Image processing with log sampling : ./retinaDemo -image lena.jpg log"<<std::endl;
+    std::cout<<"\t-Video processing : ./retinaDemo -video myMovie.mp4"<<std::endl;
+    std::cout<<"\t-Live video processing : ./retinaDemo -video"<<std::endl;
+    std::cout<<"\nPlease start again with new parameters"<<std::endl;
+    std::cout<<"****************************************************"<<std::endl;
+    std::cout<<" NOTE : this program generates the default retina parameters file 'RetinaDefaultParameters.xml'"<<std::endl;
+    std::cout<<" => you can use this to fine tune parameters and load them if you save to file 'RetinaSpecificParameters.xml'"<<std::endl;
+   }
+
+Then, start the main program and first declare a *cv::Mat* matrix in which input images will be loaded. Also allocate a *cv::VideoCapture* object ready to load video streams (if necessary)
+
+.. code-block:: cpp
+
+  int main(int argc, char* argv[]) {
+    // declare the retina input buffer... that will be fed differently in regard of the input media
+    cv::Mat inputFrame;
+    cv::VideoCapture videoCapture; // in case a video media is used, its manager is declared here
+
+
+In the main program, before processing, first check input command parameters. Here it loads a first input image coming from a single loaded image (if user chose command *-image*) or from a video stream (if user chose command *-video*). Also, if the user added *log* command at the end of its program call, the spatial logarithmic image sampling performed by the retina is taken into account by the Boolean flag *useLogSampling*.
+
+.. code-block:: cpp
+
+  // welcome message
+    std::cout<<"****************************************************"<<std::endl;
+    std::cout<<"* Retina demonstration : demonstrates the use of is a wrapper class of the Gipsa/Listic Labs retina model."<<std::endl;
+    std::cout<<"* This demo will try to load the file 'RetinaSpecificParameters.xml' (if exists).\nTo create it, copy the autogenerated template 'RetinaDefaultParameters.xml'.\nThen twaek it with your own retina parameters."<<std::endl;
+    // basic input arguments checking
+    if (argc<2)
+    {
+        help("bad number of parameter");
+        return -1;
+    }
+
+    bool useLogSampling = !strcmp(argv[argc-1], "log"); // check if user wants retina log sampling processing
+
+    std::string inputMediaType=argv[1];
+
+    //////////////////////////////////////////////////////////////////////////////
+    // checking input media type (still image, video file, live video acquisition)
+    if (!strcmp(inputMediaType.c_str(), "-image") && argc >= 3)
+    {
+        std::cout<<"RetinaDemo: processing image "<<argv[2]<<std::endl;
+        // image processing case
+        inputFrame = cv::imread(std::string(argv[2]), 1); // load image in RGB mode
+    }else
+        if (!strcmp(inputMediaType.c_str(), "-video"))
+        {
+            if (argc == 2 || (argc == 3 && useLogSampling)) // attempt to grab images from a video capture device
+            {
+                videoCapture.open(0);
+            }else// attempt to grab images from a video filestream
+            {
+                std::cout<<"RetinaDemo: processing video stream "<<argv[2]<<std::endl;
+                videoCapture.open(argv[2]);
+            }
+
+            // grab a first frame to check if everything is ok
+            videoCapture>>inputFrame;
+        }else
+        {
+            // bad command parameter
+            help("bad command parameter");
+            return -1;
+        }
+
+Once all input parameters are processed, a first image should have been loaded, if not, display error and stop program :
+
+.. code-block:: cpp
+
+    if (inputFrame.empty())
+    {
+        help("Input media could not be loaded, aborting");
+        return -1;
+    }
+
+Now, everything is ready to run the retina model. I propose here to allocate a retina instance and to manage the eventual log sampling option. The Retina constructor expects at least a cv::Size object that shows the input data size that will have to be managed. One can activate other options such as color and its related color multiplexing strategy (here Bayer multiplexing is chosen using *enum cv::bioinspired::RETINA_COLOR_BAYER*). If using log sampling, the image reduction factor (smaller output images) and log sampling strengh can be adjusted.
+
+.. code-block:: cpp
+
+    // pointer to a retina object
+    cv::Ptr<Retina> myRetina;
+
+    // if the last parameter is 'log', then activate log sampling (favour foveal vision and subsamples peripheral vision)
+    if (useLogSampling)
+    {
+        myRetina = cv::bioinspired::createRetina(inputFrame.size(), true, cv::bioinspired::RETINA_COLOR_BAYER, true, 2.0, 10.0);
+    }
+    else// -> else allocate "classical" retina :
+        myRetina = cv::bioinspired::createRetina(inputFrame.size());
+
+Once done, the proposed code writes a default xml file that contains the default parameters of the retina. This is useful to make your own config using this template. Here generated template xml file is called *RetinaDefaultParameters.xml*.
+
+.. code-block:: cpp
+
+    // save default retina parameters file in order to let you see this and maybe modify it and reload using method "setup"
+    myRetina->write("RetinaDefaultParameters.xml");
+
+In the following line, the retina attempts to load another xml file called *RetinaSpecificParameters.xml*. If you created it and introduced your own setup, it will be loaded, in the other case, default retina parameters are used.
+
+.. code-block:: cpp
+
+    // load parameters if file exists
+    myRetina->setup("RetinaSpecificParameters.xml");
+
+It is not required here but just to show it is possible, you can reset the retina buffers to zero to force it to forget past events.
+
+.. code-block:: cpp
+
+    // reset all retina buffers (imagine you close your eyes for a long time)
+    myRetina->clearBuffers();
+
+Now, it is time to run the retina ! First create some output buffers ready to receive the two retina channels outputs
+
+.. code-block:: cpp
+
+    // declare retina output buffers
+    cv::Mat retinaOutput_parvo;
+    cv::Mat retinaOutput_magno;
+
+Then, run retina in a loop, load new frames from video sequence if necessary and get retina outputs back to dedicated buffers.
+
+.. code-block:: cpp
+
+    // processing loop with no stop condition
+    while(true)
+    {
+        // if using video stream, then, grabbing a new frame, else, input remains the same
+        if (videoCapture.isOpened())
+            videoCapture>>inputFrame;
+
+        // run retina filter on the loaded input frame
+        myRetina->run(inputFrame);
+        // Retrieve and display retina output
+        myRetina->getParvo(retinaOutput_parvo);
+        myRetina->getMagno(retinaOutput_magno);
+        cv::imshow("retina input", inputFrame);
+        cv::imshow("Retina Parvo", retinaOutput_parvo);
+        cv::imshow("Retina Magno", retinaOutput_magno);
+        cv::waitKey(10);
+    }
+
+That's done ! But if you want to secure the system, take care and manage Exceptions. The retina can throw some when it sees irrelevant data (no input frame, wrong setup, etc.).
+Then, i recommend to surround all the retina code by a try/catch system like this :
+
+.. code-block:: cpp
+
+    try{
+         // pointer to a retina object
+         cv::Ptr<cv::Retina> myRetina;
+         [---]
+         // processing loop with no stop condition
+         while(true)
+         {
+             [---]
+         }
+
+    }catch(cv::Exception e)
+    {
+        std::cerr<<"Error using Retina : "<<e.what()<<std::endl;
+    }
+
+Retina parameters, what to do ?
+===============================
+
+First, it is recommended to read the reference paper :
+
+* Benoit A., Caplier A., Durette B., Herault, J., *"Using Human Visual System Modeling For Bio-Inspired Low Level Image Processing"*, Elsevier, Computer Vision and Image Understanding 114 (2010), pp. 758-773. DOI <http://dx.doi.org/10.1016/j.cviu.2010.01.011>
+
+Once done open the configuration file *RetinaDefaultParameters.xml* generated by the demo and let's have a look at it.
+
+.. code-block:: cpp
+
+    <?xml version="1.0"?>
+    <opencv_storage>
+    <OPLandIPLparvo>
+        <colorMode>1</colorMode>
+        <normaliseOutput>1</normaliseOutput>
+        <photoreceptorsLocalAdaptationSensitivity>7.5e-01</photoreceptorsLocalAdaptationSensitivity>
+        <photoreceptorsTemporalConstant>9.0e-01</photoreceptorsTemporalConstant>
+        <photoreceptorsSpatialConstant>5.7e-01</photoreceptorsSpatialConstant>
+        <horizontalCellsGain>0.01</horizontalCellsGain>
+        <hcellsTemporalConstant>0.5</hcellsTemporalConstant>
+        <hcellsSpatialConstant>7.</hcellsSpatialConstant>
+        <ganglionCellsSensitivity>7.5e-01</ganglionCellsSensitivity></OPLandIPLparvo>
+    <IPLmagno>
+        <normaliseOutput>1</normaliseOutput>
+        <parasolCells_beta>0.</parasolCells_beta>
+        <parasolCells_tau>0.</parasolCells_tau>
+        <parasolCells_k>7.</parasolCells_k>
+        <amacrinCellsTemporalCutFrequency>2.0e+00</amacrinCellsTemporalCutFrequency>
+        <V0CompressionParameter>9.5e-01</V0CompressionParameter>
+        <localAdaptintegration_tau>0.</localAdaptintegration_tau>
+        <localAdaptintegration_k>7.</localAdaptintegration_k></IPLmagno>
+    </opencv_storage>
+
+Here are some hints but actually, the best parameter setup depends more on what you want to do with the retina rather than the images input that you give to retina. Apart from the more specific case of High Dynamic Range images (HDR) that require more specific setup for specific luminance compression objective, the retina behaviors should be rather stable from content to content. Note that OpenCV is able to manage such HDR format thanks to the OpenEXR images compatibility.
+
+Then, if the application target requires details enhancement prior to specific image processing, you need to know if mean luminance information is required or not. If not, the the retina can cancel or significantly reduce its energy thus giving more visibility to higher spatial frequency details.
+
+
+Basic parameters
+----------------
+
+The most simple parameters are the following :
+
+* **colorMode** : let the retina process color information (if 1) or gray scale images (if 0). In this last case, only the first channel of the input will be processed.
+
+* **normaliseOutput** : each channel has this parameter, if value is 1, then the considered channel output is rescaled between 0 and 255. Take care in this case at the Magnocellular output level (motion/transient channel detection). Residual noise will also be rescaled !
+
+**Note :** using color requires color channels multiplexing/demultipexing which requires more processing. You can expect much faster processing using gray levels : it would require around 30 product per pixel for all the retina processes and it has recently been parallelized for multicore architectures.
+
+Photo-receptors parameters
+--------------------------
+
+The following parameters act on the entry point of the retina - photo-receptors - and impact all the following processes. These sensors are low pass spatio-temporal filters that smooth temporal and spatial data and also adjust there sensitivity to local luminance thus improving details extraction and high frequency noise canceling.
+
+* **photoreceptorsLocalAdaptationSensitivity** between 0 and 1. Values close to 1 allow high luminance log compression effect at the photo-receptors level. Values closer to 0 give a more linear sensitivity. Increased alone, it can burn the *Parvo (details channel)* output image. If adjusted in collaboration with **ganglionCellsSensitivity** images can be very contrasted whatever the local luminance there is... at the price of a naturalness decrease.
+
+* **photoreceptorsTemporalConstant** this setups the temporal constant of the low pass filter effect at the entry of the retina. High value lead to strong temporal smoothing effect : moving objects are blurred and can disappear while static object are favored. But when starting the retina processing, stable state is reached lately.
+
+* **photoreceptorsSpatialConstant** specifies the spatial constant related to photo-receptors low pass filter effect. This parameters specify the minimum allowed spatial signal period allowed in the following. Typically, this filter should cut high frequency noise. Then a 0 value doesn't cut anything noise while higher values start to cut high spatial frequencies and more and more lower frequencies... Then, do not go to high if you wanna see some details of the input images ! A good compromise for color images is 0.53 since this won't affect too much the color spectrum. Higher values would lead to gray and blurred output images.
+
+Horizontal cells parameters
+---------------------------
+
+This parameter set tunes the neural network connected to the photo-receptors, the horizontal cells. It modulates photo-receptors sensitivity and completes the processing for final spectral whitening (part of the spatial band pass effect thus favoring visual details enhancement).
+
+* **horizontalCellsGain** here is a critical parameter ! If you are not interested by the mean luminance and focus on details enhancement, then, set to zero. But if you want to keep some environment luminance data, let some low spatial frequencies pass into the system and set a higher value (<1).
+
+* **hcellsTemporalConstant** similar to photo-receptors, this acts on the temporal constant of a low pass temporal filter that smooths input data. Here, a high value generates a high retina after effect while a lower value makes the retina more reactive. This value should be lower than **photoreceptorsTemporalConstant** to limit strong retina after effects.
+
+* **hcellsSpatialConstant** is the spatial constant of the low pass filter of these cells filter. It specifies the lowest spatial frequency allowed in the following. Visually, a high value leads to very low spatial frequencies processing and leads to salient halo effects. Lower values reduce this effect but the limit is : do not go lower than the value of **photoreceptorsSpatialConstant**. Those 2 parameters actually specify the spatial band-pass of the retina.
+
+**NOTE** after the processing managed by the previous parameters, input data is cleaned from noise and luminance in already partly enhanced. The following parameters act on the last processing stages of the two outing retina signals.
+
+Parvo (details channel) dedicated parameter
+-------------------------------------------
+
+* **ganglionCellsSensitivity** specifies the strength of the final local adaptation occurring at the output of this details dedicated channel. Parameter values remain between 0 and 1. Low value tend to give a linear response while higher values enforces the remaining low contrasted areas.
+
+**Note :** this parameter can correct eventual burned images by favoring low energetic details of the visual scene, even in bright areas.
+
+IPL Magno (motion/transient channel) parameters
+-----------------------------------------------
+
+Once image information is cleaned, this channel acts as a high pass temporal filter that only selects signals related to transient signals (events, motion, etc.). A low pass spatial filter smooths extracted transient data and a final logarithmic compression enhances low transient events thus enhancing event sensitivity.
+
+* **parasolCells_beta** generally set to zero, can be considered as an amplifier gain at the entry point of this processing stage. Generally set to 0.
+
+* **parasolCells_tau** the temporal smoothing effect that can be added
+
+* **parasolCells_k** the spatial constant of the spatial filtering effect, set it at a high value to favor low spatial frequency signals that are lower subject to residual noise.
+
+* **amacrinCellsTemporalCutFrequency** specifies the temporal constant of the high pass filter. High values let slow transient events to be selected.
+
+* **V0CompressionParameter** specifies the strength of the log compression. Similar behaviors to previous description but here it enforces sensitivity of transient events.
+
+* **localAdaptintegration_tau** generally set to 0, no real use here actually
+
+* **localAdaptintegration_k** specifies the size of the area on which local adaptation is performed. Low values lead to short range local adaptation (higher sensitivity to noise), high values secure log compression.

doc/tutorials/bioinspired/table_of_content_bioinspired/table_of_content_bioinspired.rst

@@ -0,0 +1,36 @@
+.. _Table-Of-Content-Bioinspired:
+
+*bioinspired* module. Algorithms inspired from biological models
+----------------------------------------------------------------
+
+Here you will learn how to use additional modules of OpenCV defined in the "bioinspired" module.
+
+ .. include:: ../../definitions/tocDefinitions.rst
+
++
+  .. tabularcolumns:: m{100pt} m{300pt}
+  .. cssclass:: toctableopencv
+
+  =============== ======================================================
+  |RetinaDemoImg| **Title:** :ref:`Retina_Model`
+
+                  *Compatibility:* > OpenCV 2.4
+
+                  *Author:* |Author_AlexB|
+
+                  You will learn how to process images and video streams with a model of retina filter for details enhancement, spatio-temporal noise removal, luminance correction and spatio-temporal events detection.
+
+  =============== ======================================================
+
+  .. |RetinaDemoImg| image:: images/retina_TreeHdr_small.jpg
+                   :height: 90pt
+                   :width:  90pt
+
+ .. raw:: latex
+
+    \pagebreak
+
+.. toctree::
+   :hidden:
+
+   ../retina_model/retina_model

doc/tutorials/calib3d/camera_calibration/camera_calibration.rst

@@ -3,42 +3,42 @@
 Camera calibration With OpenCV
 ******************************
 
-Cameras have been around for a long-long time. However, with the introduction of the cheap *pinhole* cameras in the late 20th century, they became a common occurrence in our everyday life. Unfortunately, this cheapness comes with its price: significant distortion. Luckily, these are constants and with a calibration and some remapping we can correct this. Furthermore, with calibration you may also determinate the relation between the camera's natural units (pixels) and the real world units (for example millimeters).
+Cameras have been around for a long-long time. However, with the introduction of the cheap *pinhole* cameras in the late 20th century, they became a common occurrence in our everyday life. Unfortunately, this cheapness comes with its price: significant distortion. Luckily, these are constants and with a calibration and some remapping we can correct this. Furthermore, with calibration you may also determine the relation between the camera's natural units (pixels) and the real world units (for example millimeters).
 
 Theory
 ======
 
-For the distortion OpenCV takes into account the radial and tangential factors. For the radial one uses the following formula:
+For the distortion OpenCV takes into account the radial and tangential factors. For the radial factor one uses the following formula:
 
 .. math::
 
    x_{corrected} = x( 1 + k_1 r^2 + k_2 r^4 + k_3 r^6) \\
    y_{corrected} = y( 1 + k_1 r^2 + k_2 r^4 + k_3 r^6)
 
-So for an old pixel point at :math:`(x,y)` coordinate in the input image, for a corrected output image its position will be :math:`(x_{corrected} y_{corrected})` . The presence of the radial distortion manifests in form of the "barrel" or "fish-eye" effect.
+So for an old pixel point at :math:`(x,y)` coordinates in the input image, its position on the corrected output image will be :math:`(x_{corrected} y_{corrected})`. The presence of the radial distortion manifests in form of the "barrel" or "fish-eye" effect.
 
-Tangential distortion occurs because the image taking lenses are not perfectly parallel to the imaging plane. Correcting this is made via the formulas:
+Tangential distortion occurs because the image taking lenses are not perfectly parallel to the imaging plane. It can be corrected via the formulas:
 
 .. math::
 
    x_{corrected} = x + [ 2p_1xy + p_2(r^2+2x^2)] \\
    y_{corrected} = y + [ p_1(r^2+ 2y^2)+ 2p_2xy]
 
-So we have five distortion parameters, which in OpenCV are organized in a 5 column one row matrix:
+So we have five distortion parameters which in OpenCV are presented as one row matrix with 5 columns:
 
 .. math::
 
   Distortion_{coefficients}=(k_1 \hspace{10pt} k_2 \hspace{10pt} p_1 \hspace{10pt} p_2 \hspace{10pt} k_3)
 
-Now for the unit conversion, we use the following formula:
+Now for the unit conversion we use the following formula:
 
 .. math::
 
    \left [  \begin{matrix}   x \\   y \\  w \end{matrix} \right ] = \left [ \begin{matrix}   f_x & 0 & c_x \\  0 & f_y & c_y \\   0 & 0 & 1 \end{matrix} \right ] \left [ \begin{matrix}  X \\  Y \\   Z \end{matrix} \right ]
 
-Here the presence of the :math:`w` is cause we use a homography coordinate system (and :math:`w=Z`). The unknown parameters are :math:`f_x` and :math:`f_y` (camera focal lengths) and :math:`(c_x, c_y)` what are the optical centers expressed in pixels coordinates. If for both axes a common focal length is used with a given :math:`a` aspect ratio (usually 1), then :math:`f_y=f_x*a` and in the upper formula we will have a single :math:`f` focal length. The matrix containing these four parameters is referred to as the *camera matrix*. While the distortion coefficients are the same regardless of the camera resolutions used, these should be scaled along with the current resolution from the calibrated resolution.
+Here the presence of :math:`w` is explained by the use of homography coordinate system (and :math:`w=Z`). The unknown parameters are :math:`f_x` and :math:`f_y` (camera focal lengths) and :math:`(c_x, c_y)` which are the optical centers expressed in pixels coordinates. If for both axes a common focal length is used with a given :math:`a` aspect ratio (usually 1), then :math:`f_y=f_x*a` and in the upper formula we will have a single focal length :math:`f`. The matrix containing these four parameters is referred to as the *camera matrix*. While the distortion coefficients are the same regardless of the camera resolutions used, these should be scaled along with the current resolution from the calibrated resolution.
 
-The process of determining these two matrices is the calibration. Calculating these parameters is done by some basic geometrical equations. The equations used depend on the calibrating objects used. Currently OpenCV supports three types of object for calibration:
+The process of determining these two matrices is the calibration. Calculation of these parameters is done through basic geometrical equations. The equations used depend on the chosen calibrating objects. Currently OpenCV supports three types of objects for calibration:
 
 .. container:: enumeratevisibleitemswithsquare
 
@@ -46,7 +46,7 @@ The process of determining these two matrices is the calibration. Calculating th
    + Symmetrical circle pattern
    + Asymmetrical circle pattern
 
-Basically, you need to take snapshots of these patterns with your camera and let OpenCV find them. Each found pattern equals in a new equation. To solve the equation you need at least a predetermined number of pattern snapshots to form a well-posed equation system. This number is higher for the chessboard pattern and less for the circle ones. For example, in theory the chessboard one requires at least two. However, in practice we have a good amount of noise present in our input images, so for good results you will probably want at least 10 good snapshots of the input pattern in different position.
+Basically, you need to take snapshots of these patterns with your camera and let OpenCV find them. Each found pattern results in a new equation. To solve the equation you need at least a predetermined number of pattern snapshots to form a well-posed equation system. This number is higher for the chessboard pattern and less for the circle ones. For example, in theory the chessboard pattern requires at least two snapshots. However, in practice we have a good amount of noise present in our input images, so for good results you will probably need at least 10 good snapshots of the input pattern in different positions.
 
 Goal
 ====
@@ -55,19 +55,19 @@ The sample application will:
 
 .. container:: enumeratevisibleitemswithsquare
 
-   + Determinate the distortion matrix
-   + Determinate the camera matrix
-   + Input from Camera, Video and Image file list
-   + Configuration from XML/YAML file
+   + Determine the distortion matrix
+   + Determine the camera matrix
+   + Take input from Camera, Video and Image file list
+   + Read configuration from XML/YAML file
    + Save the results into XML/YAML file
    + Calculate re-projection error
 
 Source code
 ===========
 
-You may also find the source code in the :file:`samples/cpp/tutorial_code/calib3d/camera_calibration/` folder of the OpenCV source library or :download:`download it from here <../../../../samples/cpp/tutorial_code/calib3d/camera_calibration/camera_calibration.cpp>`. The program has a single argument. The name of its configuration file. If none given it will try to open the one named "default.xml". :download:`Here's a sample configuration file <../../../../samples/cpp/tutorial_code/calib3d/camera_calibration/in_VID5.xml>` in XML format. In the configuration file you may choose to use as input a camera, a video file or an image list. If you opt for the later one, you need to create a configuration file where you enumerate the images to use. Here's :download:`an example of this <../../../../samples/cpp/tutorial_code/calib3d/camera_calibration/VID5.xml>`. The important part to remember is that the images needs to be specified using the absolute path or the relative one from your applications working directory. You may find all this in the beforehand mentioned directory.
+You may also find the source code in the :file:`samples/cpp/tutorial_code/calib3d/camera_calibration/` folder of the OpenCV source library or :download:`download it from here <../../../../samples/cpp/tutorial_code/calib3d/camera_calibration/camera_calibration.cpp>`. The program has a single argument: the name of its configuration file. If none is given then it will try to open the one named "default.xml". :download:`Here's a sample configuration file <../../../../samples/cpp/tutorial_code/calib3d/camera_calibration/in_VID5.xml>` in XML format. In the configuration file you may choose to use camera as an input, a video file or an image list. If you opt for the last one, you will need to create a configuration file where you enumerate the images to use. Here's :download:`an example of this <../../../../samples/cpp/tutorial_code/calib3d/camera_calibration/VID5.xml>`. The important part to remember is that the images need to be specified using the absolute path or the relative one from your application's working directory. You may find all this in the samples directory mentioned above.
 
-The application starts up with reading the settings from the configuration file. Although, this is an important part of it, it has nothing to do with the subject of this tutorial: *camera calibration*. Therefore, I've chosen to do not post here the code part for that. The technical background on how to do this you can find in the :ref:`fileInputOutputXMLYAML` tutorial.
+The application starts up with reading the settings from the configuration file. Although, this is an important part of it, it has nothing to do with the subject of this tutorial: *camera calibration*. Therefore, I've chosen not to post the code for that part here. Technical background on how to do this you can find in the :ref:`fileInputOutputXMLYAML` tutorial.
 
 Explanation
 ===========
@@ -93,9 +93,9 @@ Explanation
             return -1;
       }
 
-   For this I've used simple OpenCV class input operation. After reading the file I've an additional post-process function that checks for the validity of the input. Only if all of them are good will be the *goodInput* variable true.
+   For this I've used simple OpenCV class input operation. After reading the file I've an additional post-processing function that checks validity of the input. Only if all inputs are good then *goodInput* variable will be true.
 
-#. **Get next input, if it fails or we have enough of them calibrate**. After this we have a big loop where we do the following operations: get the next image from the image list, camera or video file. If this fails or we have enough images we run the calibration process. In case of image we step out of the loop and otherwise the remaining frames will be undistorted (if the option is set) via changing from *DETECTION* mode to *CALIBRATED* one.
+#. **Get next input, if it fails or we have enough of them - calibrate**. After this we have a big loop where we do the following operations: get the next image from the image list, camera or video file. If this fails or we have enough images then we run the calibration process. In case of image we step out of the loop and otherwise the remaining frames will be undistorted (if the option is set) via changing from *DETECTION* mode to the *CALIBRATED* one.
 
    .. code-block:: cpp
 
@@ -125,7 +125,7 @@ Explanation
 
    For some cameras we may need to flip the input image. Here we do this too.
 
-#. **Find the pattern in the current input**. The formation of the equations I mentioned above consists of finding the major patterns in the input: in case of the chessboard this is their corners of the squares and for the circles, well, the circles itself. The position of these will form the result and is collected into the *pointBuf* vector.
+#. **Find the pattern in the current input**. The formation of the equations I mentioned above aims to finding major patterns in the input: in case of the chessboard this are corners of the squares and for the circles, well, the circles themselves. The position of these will form the result which will be written into the *pointBuf* vector.
 
    .. code-block:: cpp
 
@@ -146,9 +146,9 @@ Explanation
         break;
       }
 
-   Depending on the type of the input pattern you use either the :calib3d:`findChessboardCorners <findchessboardcorners>` or the :calib3d:`findCirclesGrid <findcirclesgrid>` function. For both of them you pass on the current image, the size of the board and you'll get back the positions of the patterns. Furthermore, they return a boolean variable that states if in the input we could find or not the pattern (we only need to take into account images where this is true!).
+   Depending on the type of the input pattern you use either the :calib3d:`findChessboardCorners <findchessboardcorners>` or the :calib3d:`findCirclesGrid <findcirclesgrid>` function. For both of them you pass the current image and the size of the board and you'll get the positions of the patterns. Furthermore, they return a boolean variable which states if the pattern was found in the input (we only need to take into account those images where this is true!).
 
-   Then again in case of cameras we only take camera images after an input delay time passed. This is in order to allow for the user to move the chessboard around and as getting different images. Same images mean same equations, and same equations at the calibration will form an ill-posed problem, so the calibration will fail. For square images the position of the corners are only approximate. We may improve this by calling the :feature2d:`cornerSubPix <cornersubpix>` function. This way will get a better calibration result. After this we add a valid inputs result to the *imagePoints* vector to collect all of the equations into a single container. Finally, for visualization feedback purposes we will draw the found points on the input image with the :calib3d:`findChessboardCorners <drawchessboardcorners>` function.
+   Then again in case of cameras we only take camera images when an input delay time is passed. This is done in order to allow user moving the chessboard around and getting different images. Similar images result in similar equations, and similar equations at the calibration step will form an ill-posed problem, so the calibration will fail. For square images the positions of the corners are only approximate. We may improve this by calling the :feature2d:`cornerSubPix <cornersubpix>` function. It will produce better calibration result. After this we add a valid inputs result to the *imagePoints* vector to collect all of the equations into a single container. Finally, for visualization feedback purposes we will draw the found points on the input image using :calib3d:`findChessboardCorners <drawchessboardcorners>` function.
 
    .. code-block:: cpp
 
@@ -175,7 +175,7 @@ Explanation
               drawChessboardCorners( view, s.boardSize, Mat(pointBuf), found );
         }
 
-#. **Show state and result for the user, plus command line control of the application**. The showing part consists of a text output on the live feed, and for video or camera input to show the "capturing" frame we simply bitwise negate the input image.
+#. **Show state and result to the user, plus command line control of the application**. This  part shows text output on the image.
 
    .. code-block:: cpp
 
@@ -199,7 +199,7 @@ Explanation
       if( blinkOutput )
          bitwise_not(view, view);
 
-   If we only ran the calibration and got the camera matrix plus the distortion coefficients we may just as correct the image with the :imgproc_geometric:`undistort <undistort>` function:
+   If we ran calibration and got camera's matrix with the distortion coefficients we may want to correct the image using :imgproc_geometric:`undistort <undistort>` function:
 
    .. code-block:: cpp
 
@@ -212,7 +212,7 @@ Explanation
       //------------------------------ Show image and check for input commands -------------------
       imshow("Image View", view);
 
-   Then we wait for an input key and if this is *u* we toggle the distortion removal, if it is *g* we start all over the detection process (or simply start it), and finally for the *ESC* key quit the application:
+   Then we wait for an input key and if this is *u* we toggle the distortion removal, if it is *g* we start again the detection process, and finally for the *ESC* key we quit the application:
 
    .. code-block:: cpp
 
@@ -229,7 +229,7 @@ Explanation
         imagePoints.clear();
       }
 
-#. **Show the distortion removal for the images too**. When you work with an image list it is not possible to remove the distortion inside the loop. Therefore, you must append this after the loop. Taking advantage of this now I'll expand the :imgproc_geometric:`undistort <undistort>` function, which is in fact first a call of the :imgproc_geometric:`initUndistortRectifyMap <initundistortrectifymap>` to find out the transformation matrices and then doing the transformation with the :imgproc_geometric:`remap <remap>` function. Because, after a successful calibration the map calculation needs to be done only once, by using this expanded form you may speed up your application:
+#. **Show the distortion removal for the images too**. When you work with an image list it is not possible to remove the distortion inside the loop. Therefore, you must do this after the loop. Taking advantage of this now I'll expand the :imgproc_geometric:`undistort <undistort>` function, which is in fact first calls :imgproc_geometric:`initUndistortRectifyMap <initundistortrectifymap>` to find transformation matrices and then performs transformation using :imgproc_geometric:`remap <remap>` function. Because, after successful calibration map calculation needs to be done only once, by using this expanded form you may speed up your application:
 
    .. code-block:: cpp
 
@@ -256,7 +256,7 @@ Explanation
 The calibration and save
 ========================
 
-Because the calibration needs to be only once per camera it makes sense to save them after a successful calibration. This way later on you can just load these values into your program. Due to this we first make the calibration, and if it succeeds we save the result into an OpenCV style XML or YAML file, depending on the extension you give in the configuration file.
+Because the calibration needs to be done only once per camera, it makes sense to save it after a successful calibration. This way later on you can just load these values into your program. Due to this we first make the calibration, and if it succeeds we save the result into an OpenCV style XML or YAML file, depending on the extension you give in the configuration file.
 
 Therefore in the first function we just split up these two processes. Because we want to save many of the calibration variables we'll create these variables here and pass on both of them to the calibration and saving function. Again, I'll not show the saving part as that has little in common with the calibration. Explore the source file in order to find out how and what:
 
@@ -280,7 +280,7 @@ Therefore in the first function we just split up these two processes. Because we
     return ok;
    }
 
-We do the calibration with the help of the :calib3d:`calibrateCamera <calibratecamera>` function. This has the following parameters:
+We do the calibration with the help of the :calib3d:`calibrateCamera <calibratecamera>` function. It has the following parameters:
 
 .. container:: enumeratevisibleitemswithsquare
 
@@ -318,11 +318,11 @@ We do the calibration with the help of the :calib3d:`calibrateCamera <calibratec
         calcBoardCornerPositions(s.boardSize, s.squareSize, objectPoints[0], s.calibrationPattern);
         objectPoints.resize(imagePoints.size(),objectPoints[0]);
 
-   + The image points. This is a vector of *Point2f* vector that for each input image contains where the important points (corners for chessboard, and center of circles for the circle patterns) were found. We already collected this from what the :calib3d:`findChessboardCorners <findchessboardcorners>` or the :calib3d:`findCirclesGrid <findcirclesgrid>` function returned. We just need to pass it on.
+   + The image points. This is a vector of *Point2f* vector which for each input image contains coordinates of the important points (corners for chessboard and centers of the circles for the circle pattern). We have already collected this from :calib3d:`findChessboardCorners <findchessboardcorners>` or :calib3d:`findCirclesGrid <findcirclesgrid>` function. We just need to pass it on.
 
    + The size of the image acquired from the camera, video file or the images.
 
-   + The camera matrix. If we used the fix aspect ratio option we need to set the :math:`f_x` to zero:
+   + The camera matrix. If we used the fixed aspect ratio option we need to set the :math:`f_x` to zero:
 
      .. code-block:: cpp
 
@@ -336,16 +336,16 @@ We do the calibration with the help of the :calib3d:`calibrateCamera <calibratec
 
         distCoeffs = Mat::zeros(8, 1, CV_64F);
 
-   + The function will calculate for all the views the rotation and translation vector that transform the object points (given in the model coordinate space) to the image points (given in the world coordinate space). The 7th and 8th parameters are an output vector of matrices containing in the ith position the rotation and translation vector for the ith object point to the ith image point.
+   + For all the views the function will calculate rotation and translation vectors which transform the object points (given in the model coordinate space) to the image points (given in the world coordinate space). The 7-th and 8-th parameters are the output vector of matrices containing in the i-th position the rotation and translation vector for the i-th object point to the i-th image point.
 
-   + The final argument is a flag. You need to specify here options like fix the aspect ratio for the focal length, assume zero tangential distortion or to fix the principal point.
+   + The final argument is the flag. You need to specify here options like fix the aspect ratio for the focal length, assume zero tangential distortion or to fix the principal point.
 
    .. code-block:: cpp
 
      double rms = calibrateCamera(objectPoints, imagePoints, imageSize, cameraMatrix,
                                  distCoeffs, rvecs, tvecs, s.flag|CV_CALIB_FIX_K4|CV_CALIB_FIX_K5);
 
-   + The function returns the average re-projection error. This number gives a good estimation of just how exact is the found parameters. This should be as close to zero as possible. Given the intrinsic, distortion, rotation and translation matrices we may calculate the error for one view by using the :calib3d:`projectPoints <projectpoints>` to first transform the object point to image point. Then we calculate the absolute norm between what we got with our transformation and the corner/circle finding algorithm. To find the average error we calculate the arithmetical mean of the errors calculate for all the calibration images.
+   + The function returns the average re-projection error. This number gives a good estimation of precision of the found parameters. This should be as close to zero as possible. Given the intrinsic, distortion, rotation and translation matrices we may calculate the error for one view by using the :calib3d:`projectPoints <projectpoints>` to first transform the object point to image point. Then we calculate the absolute norm between what we got with our transformation and the corner/circle finding algorithm. To find the average error we calculate the arithmetical mean of the errors calculated for all the calibration images.
 
      .. code-block:: cpp
 
@@ -378,25 +378,25 @@ We do the calibration with the help of the :calib3d:`calibrateCamera <calibratec
 Results
 =======
 
-Let there be :download:`this input chessboard pattern <../../../pattern.png>` that has a size of 9 X 6. I've used an AXIS IP camera to create a couple of snapshots of the board and saved it into a VID5 directory. I've put this inside the :file:`images/CameraCalibraation` folder of my working directory and created the following :file:`VID5.XML` file that describes which images to use:
+Let there be :download:`this input chessboard pattern <../../../pattern.png>` which has a size of 9 X 6. I've used an AXIS IP camera to create a couple of snapshots of the board and saved it into VID5 directory. I've put this inside the :file:`images/CameraCalibration` folder of my working directory and created the following :file:`VID5.XML` file that describes which images to use:
 
 .. code-block:: xml
 
    <?xml version="1.0"?>
    <opencv_storage>
    <images>
-   images/CameraCalibraation/VID5/xx1.jpg
-   images/CameraCalibraation/VID5/xx2.jpg
-   images/CameraCalibraation/VID5/xx3.jpg
-   images/CameraCalibraation/VID5/xx4.jpg
-   images/CameraCalibraation/VID5/xx5.jpg
-   images/CameraCalibraation/VID5/xx6.jpg
-   images/CameraCalibraation/VID5/xx7.jpg
-   images/CameraCalibraation/VID5/xx8.jpg
+   images/CameraCalibration/VID5/xx1.jpg
+   images/CameraCalibration/VID5/xx2.jpg
+   images/CameraCalibration/VID5/xx3.jpg
+   images/CameraCalibration/VID5/xx4.jpg
+   images/CameraCalibration/VID5/xx5.jpg
+   images/CameraCalibration/VID5/xx6.jpg
+   images/CameraCalibration/VID5/xx7.jpg
+   images/CameraCalibration/VID5/xx8.jpg
    </images>
    </opencv_storage>
 
-Then specified the :file:`images/CameraCalibraation/VID5/VID5.XML` as input in the configuration file. Here's a chessboard pattern found during the runtime of the application:
+Then passed :file:`images/CameraCalibration/VID5/VID5.XML` as an input in the configuration file. Here's a chessboard pattern found during the runtime of the application:
 
 .. image:: images/fileListImage.jpg
    :alt: A found chessboard
@@ -433,7 +433,7 @@ In both cases in the specified output XML/YAML file you'll find the camera and d
     -4.1802327176423804e-001 5.0715244063187526e-001 0. 0.
     -5.7843597214487474e-001</data></Distortion_Coefficients>
 
-Add these values as constants to your program, call the :imgproc_geometric:`initUndistortRectifyMap <initundistortrectifymap>` and the :imgproc_geometric:`remap <remap>` function to remove distortion and enjoy distortion free inputs with cheap and low quality cameras.
+Add these values as constants to your program, call the :imgproc_geometric:`initUndistortRectifyMap <initundistortrectifymap>` and the :imgproc_geometric:`remap <remap>` function to remove distortion and enjoy distortion free inputs for cheap and low quality cameras.
 
 You may observe a runtime instance of this on the `YouTube here <https://www.youtube.com/watch?v=ViPN810E0SU>`_.
 

doc/tutorials/calib3d/camera_calibration_square_chess/camera_calibration_square_chess.rst

@@ -59,4 +59,4 @@ Now, let us write a code that detects a chessboard in a new image and finds its
 #.
     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?
+Question: how to calculate the distance from the camera origin to any of the corners?

doc/tutorials/core/file_input_output_with_xml_yml/file_input_output_with_xml_yml.rst

@@ -277,4 +277,3 @@ You may observe a runtime instance of this on the `YouTube here <https://www.you
   <div align="center">
   <iframe title="File Input and Output using XML and YAML files in OpenCV" width="560" height="349" src="http://www.youtube.com/embed/A4yqVnByMMM?rel=0&loop=1" frameborder="0" allowfullscreen align="middle"></iframe>
   </div>
-

doc/tutorials/core/interoperability_with_OpenCV_1/interoperability_with_OpenCV_1.rst

@@ -127,6 +127,3 @@ You may observe a runtime instance of this on the `YouTube here <https://www.you
   <div align="center">
   <iframe title="Interoperability with OpenCV 1" width="560" height="349" src="http://www.youtube.com/embed/qckm-zvo31w?rel=0&loop=1" frameborder="0" allowfullscreen align="middle"></iframe>
   </div>
-
-
-

doc/tutorials/core/mat_the_basic_image_container/mat_the_basic_image_container.rst

@@ -143,7 +143,7 @@ Although *Mat* works really well as an image container, it is also a general mat
 
     You cannot initialize the matrix values with this construction. It will only reallocate its matrix data memory if the new size will not fit into the old one.
 
-   + MATLAB style initializer: :basicstructures:`zeros() <mat-zeros>`, :basicstructures:`ones() <mat-ones>`, ::basicstructures:`eyes() <mat-eye>`. Specify size and data type to use:
+   + MATLAB style initializer: :basicstructures:`zeros() <mat-zeros>`, :basicstructures:`ones() <mat-ones>`, :basicstructures:`eye() <mat-eye>`. Specify size and data type to use:
 
      .. literalinclude:: ../../../../samples/cpp/tutorial_code/core/mat_the_basic_image_container/mat_the_basic_image_container.cpp
         :language: cpp

doc/tutorials/core/table_of_content_core/table_of_content_core.rst

@@ -218,4 +218,4 @@ Here you will learn the about the basic building blocks of the library. A must r
    ../random_generator_and_text/random_generator_and_text
    ../discrete_fourier_transform/discrete_fourier_transform
    ../file_input_output_with_xml_yml/file_input_output_with_xml_yml
-   ../interoperability_with_OpenCV_1/interoperability_with_OpenCV_1
+   ../interoperability_with_OpenCV_1/interoperability_with_OpenCV_1

doc/tutorials/definitions/README.txt

@@ -1 +1 @@
-Include in this directory only defintion files. None of the reST files entered here will be parsed by the Sphinx Builder.
+Include in this directory only defintion files. None of the reST files entered here will be parsed by the Sphinx Builder.

doc/tutorials/definitions/noContent.rst

@@ -1,3 +1,3 @@
 
 .. 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 <>`.
+   Unfortunetly we have no tutorials into this section. And you can help us with that, since OpenCV is a community effort. If you have a tutorial suggestion or you have written a tutorial yourself (or coded a sample code) that you would like to see here, please contact follow these instructions: :ref:`howToWriteTutorial` and :how_to_contribute:`How to contribute <>`.

doc/tutorials/features2d/feature_description/feature_description.rst

@@ -100,6 +100,3 @@ Result
    .. image:: images/Feature_Description_BruteForce_Result.jpg
       :align: center
       :height: 200pt
-
-
-

doc/tutorials/features2d/feature_detection/feature_detection.rst

@@ -31,6 +31,7 @@ This tutorial code's is shown lines below. You can also download it from `here <
    #include "opencv2/core.hpp"
    #include "opencv2/features2d.hpp"
    #include "opencv2/highgui.hpp"
+   #include "opencv2/nonfree.hpp"
 
    using namespace cv;
 
@@ -94,4 +95,3 @@ Result
    .. image:: images/Feature_Detection_Result_b.jpg
       :align: center
       :height: 200pt
-

doc/tutorials/features2d/feature_flann_matcher/feature_flann_matcher.rst

@@ -28,6 +28,7 @@ This tutorial code's is shown lines below. You can also download it from `here <
    #include "opencv2/core.hpp"
    #include "opencv2/features2d.hpp"
    #include "opencv2/highgui.hpp"
+   #include "opencv2/nonfree.hpp"
 
    using namespace cv;
 

doc/tutorials/features2d/feature_homography/feature_homography.rst

@@ -30,6 +30,7 @@ This tutorial code's is shown lines below. You can also download it from `here <
    #include "opencv2/features2d.hpp"
    #include "opencv2/highgui.hpp"
    #include "opencv2/calib3d.hpp"
+   #include "opencv2/nonfree.hpp"
 
    using namespace cv;
 
@@ -145,4 +146,3 @@ Result
    .. image:: images/Feature_Homography_Result.jpg
       :align: center
       :height: 200pt
-

doc/tutorials/features2d/table_of_content_features2d/table_of_content_features2d.rst

@@ -201,4 +201,3 @@ Learn about how to use the feature points  detectors, descriptors and matching f
    ../feature_flann_matcher/feature_flann_matcher
    ../feature_homography/feature_homography
    ../detection_of_planar_objects/detection_of_planar_objects
-

doc/tutorials/features2d/trackingmotion/corner_subpixeles/corner_subpixeles.rst

@@ -135,4 +135,3 @@ Here is the result:
 
 .. image:: images/Corner_Subpixeles_Result.jpg
               :align: center
-

doc/tutorials/features2d/trackingmotion/generic_corner_detector/generic_corner_detector.rst

@@ -37,4 +37,3 @@ Result
 
 .. image:: images/My_Shi_Tomasi_corner_detector_Result.jpg
               :align: center
-

doc/tutorials/features2d/trackingmotion/good_features_to_track/good_features_to_track.rst

@@ -118,5 +118,3 @@ Result
 
 .. image:: images/Feature_Detection_Result_a.jpg
               :align: center
-
-

doc/tutorials/features2d/trackingmotion/harris_detector/harris_detector.rst

@@ -243,5 +243,3 @@ The detected corners are surrounded by a small black circle
 
 .. image:: images/Harris_Detector_Result.jpg
               :align: center
-
-

doc/tutorials/general/table_of_content_general/table_of_content_general.rst

@@ -10,4 +10,3 @@ These tutorials are the bottom of the iceberg as they link together multiple of
 .. raw:: latex
 
    \pagebreak
-

doc/tutorials/highgui/table_of_content_highgui/table_of_content_highgui.rst

@@ -74,4 +74,4 @@ This section contains valuable tutorials about how to read/save your image/video
 
    ../trackbar/trackbar
    ../video-input-psnr-ssim/video-input-psnr-ssim
-   ../video-write/video-write
+   ../video-write/video-write

doc/tutorials/highgui/trackbar/trackbar.rst

@@ -152,8 +152,3 @@ Result
   .. image:: images/Adding_Trackbars_Tutorial_Result_1.jpg
      :alt: Adding Trackbars - Lena
      :align: center
-
-
-
-
-

doc/tutorials/imgproc/histograms/histogram_calculation/histogram_calculation.rst

@@ -329,4 +329,3 @@ Result
 
      .. image:: images/Histogram_Calculation_Result.jpg
               :align: center
-

doc/tutorials/imgproc/histograms/template_matching/template_matching.rst

@@ -369,4 +369,3 @@ Results
 
    .. image:: images/Template_Matching_Image_Result.jpg
             :align: center
-

doc/tutorials/imgproc/imgtrans/canny_detector/canny_detector.rst

@@ -282,6 +282,3 @@ Result
            :align: center
 
 * Notice how the image is superposed to the black background on the edge regions.
-
-
-

doc/tutorials/imgproc/imgtrans/hough_circle/hough_circle.rst

@@ -40,7 +40,7 @@ Code
    * Display the detected circle in a window.
 
    .. |TutorialHoughCirclesSimpleDownload| replace:: here
-   .. _TutorialHoughCirclesSimpleDownload: http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/cpp/houghlines.cpp
+   .. _TutorialHoughCirclesSimpleDownload: http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/cpp/houghcircles.cpp
    .. |TutorialHoughCirclesFancyDownload| replace:: here
    .. _TutorialHoughCirclesFancyDownload: http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/cpp/tutorial_code/ImgTrans/HoughCircle_Demo.cpp
 

doc/tutorials/imgproc/imgtrans/hough_lines/hough_lines.rst

@@ -290,4 +290,3 @@ We get the following result by using the Probabilistic Hough Line Transform:
    :align: center
 
 You may observe that the number of lines detected vary while you change the *threshold*. The explanation is sort of evident: If you establish a higher threshold, fewer lines will be detected (since you will need more points to declare a line detected).
-

doc/tutorials/imgproc/imgtrans/remap/remap.rst

@@ -311,4 +311,3 @@ Result
          :alt: Result 0 for remapping
          :width: 250pt
          :align: center
-

doc/tutorials/imgproc/imgtrans/warp_affine/warp_affine.rst

@@ -306,4 +306,3 @@ Result
             :alt: Original image
             :width: 250pt
             :align: center
-

doc/tutorials/imgproc/opening_closing_hats/opening_closing_hats.rst

@@ -279,4 +279,3 @@ Results
   .. image:: images/Morphology_2_Tutorial_Cover.jpg
      :alt: Morphology 2: Result sample
      :align: center
-

doc/tutorials/imgproc/pyramids/pyramids.rst

@@ -259,5 +259,3 @@ Results
   .. image:: images/Pyramids_Tutorial_PyrUp_Result.jpg
      :alt: Pyramids: PyrUp Result
      :align: center
-
-

doc/tutorials/imgproc/shapedescriptors/bounding_rects_circles/bounding_rects_circles.rst

@@ -121,4 +121,3 @@ Result
 
    .. |BRC_1|  image:: images/Bounding_Rects_Circles_Result.jpg
                     :align: middle
-

doc/tutorials/imgproc/shapedescriptors/bounding_rotated_ellipses/bounding_rotated_ellipses.rst

@@ -123,4 +123,3 @@ Result
 
    .. |BRE_1|  image:: images/Bounding_Rotated_Ellipses_Result.jpg
                     :align: middle
-

doc/tutorials/imgproc/shapedescriptors/find_contours/find_contours.rst

@@ -104,4 +104,3 @@ Result
 
    .. |contour_1|  image:: images/Find_Contours_Result.jpg
                      :align: middle
-

doc/tutorials/imgproc/shapedescriptors/hull/hull.rst

@@ -113,4 +113,3 @@ Result
 
    .. |Hull_1|  image:: images/Hull_Result.jpg
                      :align: middle
-

doc/tutorials/imgproc/shapedescriptors/moments/moments.rst

@@ -133,4 +133,3 @@ Result
    .. |MU_2|  image:: images/Moments_Result2.jpg
                     :width: 250pt
                     :align: middle
-

doc/tutorials/imgproc/shapedescriptors/point_polygon_test/point_polygon_test.rst

@@ -114,4 +114,3 @@ Result
 
    .. |PPT_1|  image:: images/Point_Polygon_Test_Result.jpg
                     :align: middle
-

doc/tutorials/imgproc/table_of_content_imgproc/table_of_content_imgproc.rst

@@ -539,6 +539,3 @@ In this section you will learn about the image processing (manipulation) functio
    ../shapedescriptors/bounding_rotated_ellipses/bounding_rotated_ellipses
    ../shapedescriptors/moments/moments
    ../shapedescriptors/point_polygon_test/point_polygon_test
-
-
-

doc/tutorials/introduction/android_binary_package/O4A_SDK.rst

@@ -48,10 +48,10 @@ The structure of package contents looks as follows:
 
 ::
 
-    OpenCV-2.4.5-android-sdk
+    OpenCV-2.4.6-android-sdk
     |_ apk
-    |   |_ OpenCV_2.4.5_binary_pack_armv7a.apk
-    |   |_ OpenCV_2.4.5_Manager_2.7_XXX.apk
+    |   |_ OpenCV_2.4.6_binary_pack_armv7a.apk
+    |   |_ OpenCV_2.4.6_Manager_2.9_XXX.apk
     |
     |_ doc
     |_ samples
@@ -98,7 +98,7 @@ The structure of package contents looks as follows:
 * :file:`doc` folder contains various OpenCV documentation in PDF format.
   It's also available online at http://docs.opencv.org.
 
-  .. note:: The most recent docs (nightly build) are at http://docs.opencv.org/trunk/.
+  .. note:: The most recent docs (nightly build) are at http://docs.opencv.org/2.4.
             Generally, it's more up-to-date, but can refer to not-yet-released functionality.
 
 .. TODO: I'm not sure that this is the best place to talk about OpenCV Manager
@@ -157,10 +157,10 @@ Get the OpenCV4Android SDK
 
    .. code-block:: bash
 
-      unzip ~/Downloads/OpenCV-2.4.5-android-sdk.zip
+      unzip ~/Downloads/OpenCV-2.4.6-android-sdk.zip
 
-.. |opencv_android_bin_pack| replace:: :file:`OpenCV-2.4.5-android-sdk.zip`
-.. _opencv_android_bin_pack_url: http://sourceforge.net/projects/opencvlibrary/files/opencv-android/2.4.5/OpenCV-2.4.5-android-sdk.zip/download
+.. |opencv_android_bin_pack| replace:: :file:`OpenCV-2.4.6-android-sdk.zip`
+.. _opencv_android_bin_pack_url: http://sourceforge.net/projects/opencvlibrary/files/opencv-android/2.4.6/OpenCV-2.4.6-android-sdk.zip/download
 .. |opencv_android_bin_pack_url| replace:: |opencv_android_bin_pack|
 .. |seven_zip| replace:: 7-Zip
 .. _seven_zip: http://www.7-zip.org/
@@ -295,7 +295,7 @@ Well, running samples from Eclipse is very simple:
   .. code-block:: sh
     :linenos:
 
-    <Android SDK path>/platform-tools/adb install <OpenCV4Android SDK path>/apk/OpenCV_2.4.5_Manager_2.7_armv7a-neon.apk
+    <Android SDK path>/platform-tools/adb install <OpenCV4Android SDK path>/apk/OpenCV_2.4.6_Manager_2.9_armv7a-neon.apk
 
   .. note:: ``armeabi``, ``armv7a-neon``, ``arm7a-neon-android8``, ``mips`` and ``x86`` stand for
             platform targets:

doc/tutorials/introduction/android_binary_package/dev_with_OCV_on_Android.rst

@@ -55,14 +55,14 @@ Manager to access OpenCV libraries externally installed in the target system.
    :guilabel:`File -> Import -> Existing project in your workspace`.
 
    Press :guilabel:`Browse`  button and locate OpenCV4Android SDK
-   (:file:`OpenCV-2.4.5-android-sdk/sdk`).
+   (:file:`OpenCV-2.4.6-android-sdk/sdk`).
 
    .. image:: images/eclipse_opencv_dependency0.png
         :alt: Add dependency from OpenCV library
         :align: center
 
 #. In application project add a reference to the OpenCV Java SDK in
-   :guilabel:`Project -> Properties -> Android -> Library -> Add` select ``OpenCV Library - 2.4.5``.
+   :guilabel:`Project -> Properties -> Android -> Library -> Add` select ``OpenCV Library - 2.4.6``.
 
    .. image:: images/eclipse_opencv_dependency1.png
         :alt: Add dependency from OpenCV library
@@ -101,7 +101,7 @@ See the "15-puzzle" OpenCV sample for details.
         public void onResume()
         {
             super.onResume();
-            OpenCVLoader.initAsync(OpenCVLoader.OPENCV_VERSION_2_4_5, this, mLoaderCallback);
+            OpenCVLoader.initAsync(OpenCVLoader.OPENCV_VERSION_2_4_6, this, mLoaderCallback);
         }
 
         ...
@@ -128,27 +128,27 @@ described above.
 #. Add the OpenCV library project to your workspace the same way as for the async initialization
    above. Use menu :guilabel:`File -> Import -> Existing project in your workspace`,
    press :guilabel:`Browse` button and select OpenCV SDK path
-   (:file:`OpenCV-2.4.5-android-sdk/sdk`).
+   (:file:`OpenCV-2.4.6-android-sdk/sdk`).
 
    .. image:: images/eclipse_opencv_dependency0.png
         :alt: Add dependency from OpenCV library
         :align: center
 
 #. In the application project add a reference to the OpenCV4Android SDK in
-   :guilabel:`Project -> Properties -> Android -> Library -> Add` select ``OpenCV Library - 2.4.5``;
+   :guilabel:`Project -> Properties -> Android -> Library -> Add` select ``OpenCV Library - 2.4.6``;
 
    .. image:: images/eclipse_opencv_dependency1.png
        :alt: Add dependency from OpenCV library
        :align: center
 
 #. If your application project **doesn't have a JNI part**, just copy the corresponding OpenCV
-   native libs from :file:`<OpenCV-2.4.5-android-sdk>/sdk/native/libs/<target_arch>` to your
+   native libs from :file:`<OpenCV-2.4.6-android-sdk>/sdk/native/libs/<target_arch>` to your
    project directory to folder :file:`libs/<target_arch>`.
 
    In case of the application project **with a JNI part**, instead of manual libraries copying you
    need to modify your ``Android.mk`` file:
    add the following two code lines after the ``"include $(CLEAR_VARS)"`` and before
-   ``"include path_to_OpenCV-2.4.5-android-sdk/sdk/native/jni/OpenCV.mk"``
+   ``"include path_to_OpenCV-2.4.6-android-sdk/sdk/native/jni/OpenCV.mk"``
 
    .. code-block:: make
       :linenos:
@@ -221,7 +221,7 @@ taken:
 
    .. code-block:: make
 
-      include C:\Work\OpenCV4Android\OpenCV-2.4.5-android-sdk\sdk\native\jni\OpenCV.mk
+      include C:\Work\OpenCV4Android\OpenCV-2.4.6-android-sdk\sdk\native\jni\OpenCV.mk
 
    Should be inserted into the :file:`jni/Android.mk` file **after** this line:
 
@@ -379,7 +379,7 @@ result.
        public void onResume()
        {
            super.onResume();
-           OpenCVLoader.initAsync(OpenCVLoader.OPENCV_VERSION_2_4_3, this, mLoaderCallback);
+           OpenCVLoader.initAsync(OpenCVLoader.OPENCV_VERSION_2_4_6, this, mLoaderCallback);
        }
 
 #. Defines that your activity implements ``CvViewFrameListener2`` interface and fix activity related

doc/tutorials/introduction/ios_install/ios_install.rst

@@ -37,7 +37,7 @@ Building OpenCV from Source, using CMake and Command Line
     .. code-block:: bash
 
        cd ~/<my_working_directory>
-       python opencv/ios/build_framework.py ios
+       python opencv/platforms/ios/build_framework.py ios
 
 If everything's fine, a few minutes later you will get ~/<my_working_directory>/ios/opencv2.framework. You can add this framework to your Xcode projects.
 

doc/tutorials/introduction/linux_eclipse/linux_eclipse.rst

@@ -245,6 +245,3 @@ Say you have or create a new file, *helloworld.cpp* in a directory called *foo*:
  a. You can also optionally modify the ``Build command:`` from ``make`` to something like ``make VERBOSE=1 -j4`` which tells the compiler to produce detailed symbol files for debugging and also to compile in 4 parallel threads.
 
 #. Done!
-
-
-

doc/tutorials/introduction/linux_install/linux_install.rst

@@ -80,4 +80,3 @@ Building OpenCV from Source Using CMake, Using the Command Line
 .. 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/windows_install/windows_install.rst

@@ -292,7 +292,7 @@ Building the library
 
    This will create an *Install* directory inside the *Build* one collecting all the built binaries into a single place. Use this only after you built both the *Release* and *Debug* versions.
 
-   To test your build just go into the :file:`Build/bin/Debug` or :file:`Build/bin/Release` directory and start a couple of applications like the *contours.exe*. If they run, you are done. Otherwise, something definitely went awfully wrong. In this case you should contact us via our :opencv_group:`user group <>`.
+   To test your build just go into the :file:`Build/bin/Debug` or :file:`Build/bin/Release` directory and start a couple of applications like the *contours.exe*. If they run, you are done. Otherwise, something definitely went awfully wrong. In this case you should contact us at our :opencv_qa:`Q&A forum <>`.
    If everything is okay the *contours.exe* output should resemble the following image (if built with Qt support):
 
    .. image:: images/WindowsQtContoursOutput.png
@@ -312,9 +312,13 @@ First we set an enviroment variable to make easier our work. This will hold the
 
 ::
 
-   setx -m OPENCV_DIR D:\OpenCV\Build\x86\vc10
+   setx -m OPENCV_DIR D:\OpenCV\Build\x86\vc10     (suggested for Visual Studio 2010 - 32 bit Windows)
+   setx -m OPENCV_DIR D:\OpenCV\Build\x64\vc10     (suggested for Visual Studio 2010 - 64 bit Windows)
 
-Here the directory is where you have your OpenCV binaries (*extracted* or *built*). You can have different platform (e.g. x64 instead of x86) or compiler type, so substitute appropriate value. Inside this you should have folders like *bin* and *include*. The -m should be added if you wish to make the settings computer wise, instead of user wise.
+   setx -m OPENCV_DIR D:\OpenCV\Build\x86\vc11     (suggested for Visual Studio 2012 - 32 bit Windows)
+   setx -m OPENCV_DIR D:\OpenCV\Build\x64\vc11     (suggested for Visual Studio 2012 - 64 bit Windows)
+
+Here the directory is where you have your OpenCV binaries (*extracted* or *built*). You can have different platform (e.g. x64 instead of x86) or compiler type, so substitute appropriate value. Inside this you should have two folders called *lib* and *bin*. The -m should be added if you wish to make the settings computer wise, instead of user wise.
 
 If you built static libraries then you are done. Otherwise, you need to add the *bin* folders path to the systems path. This is cause you will use the OpenCV library in form of *\"Dynamic-link libraries\"* (also known as **DLL**). Inside these are stored all the algorithms and information the OpenCV library contains. The operating system will load them only on demand, during runtime. However, to do this he needs to know where they are. The systems **PATH** contains a list of folders where DLLs can be found. Add the OpenCV library path to this and the OS will know where to look if he ever needs the OpenCV binaries. Otherwise, you will need to copy the used DLLs right beside the applications executable file (*exe*) for the OS to find it, which is highly unpleasent if you work on many projects. To do this start up again the |PathEditor|_ and add the following new entry (right click in the application to bring up the menu):
 

doc/tutorials/introduction/windows_visual_studio_Opencv/windows_visual_studio_Opencv.rst

@@ -52,7 +52,7 @@ Use for example the *OpenCV_Debug* name. Then by selecting the sheet :menuselect
 
 .. code-block:: bash
 
-   $(OPENCV_DIR)\include
+   $(OPENCV_DIR)\..\..\include
 
 .. image:: images/PropertySheetOpenCVInclude.jpg
    :alt: Add the include dir like this.
@@ -64,7 +64,7 @@ Next go to the :menuselection:`Linker --> General` and under the *"Additional Li
 
 .. code-block:: bash
 
-   $(OPENCV_DIR)\libs
+   $(OPENCV_DIR)\lib
 
 .. image:: images/PropertySheetOpenCVLib.jpg
    :alt: Add the library folder like this.
@@ -86,7 +86,7 @@ The names of the libraries are as follow:
 
    opencv_(The Name of the module)(The version Number of the library you use)d.lib
 
-A full list, for the currently latest trunk version would contain:
+A full list, for the latest version would contain:
 
 .. code-block:: bash
 

doc/tutorials/ios/hello/hello.rst

@@ -73,4 +73,3 @@ 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

@@ -127,4 +127,4 @@ Check out an instance of running code with more Image Effects on `YouTube <http:
 
   <div align="center">
  <iframe width="560" height="350" src="http://www.youtube.com/embed/Ko3K_xdhJ1I" frameborder="0" allowfullscreen></iframe>
-  </div>
+  </div>

doc/tutorials/ml/introduction_to_svm/introduction_to_svm.rst

@@ -129,7 +129,7 @@ Explanation
 
 3. **Train the SVM**
 
-   We call the method `CvSVM::train <http://opencv.itseez.com/modules/ml/doc/support_vector_machines.html#cvsvm-train>`_ to build the SVM model.
+   We call the method `CvSVM::train <http://docs.opencv.org/modules/ml/doc/support_vector_machines.html#cvsvm-train>`_ to build the SVM model.
 
    .. code-block:: cpp
 
@@ -185,4 +185,3 @@ Results
 .. image:: images/result.png
   :alt: The seperated planes
   :align: center
-

doc/tutorials/objdetect/cascade_classifier/cascade_classifier.rst

@@ -26,91 +26,90 @@ This tutorial code's is shown lines below. You can also download it from `here <
 
 .. code-block:: cpp
 
-   #include "opencv2/objdetect.hpp"
-   #include "opencv2/highgui.hpp"
-   #include "opencv2/imgproc.hpp"
+    #include "opencv2/objdetect.hpp"
+    #include "opencv2/highgui.hpp"
+    #include "opencv2/imgproc.hpp"
 
-   #include <iostream>
-   #include <stdio.h>
+    #include <iostream>
+    #include <stdio.h>
 
-   using namespace std;
-   using namespace cv;
+    using namespace std;
+    using namespace cv;
 
-   /** Function Headers */
-   void detectAndDisplay( Mat frame );
+    /** Function Headers */
+    void detectAndDisplay( Mat frame );
 
-   /** Global variables */
-   String face_cascade_name = "haarcascade_frontalface_alt.xml";
-   String eyes_cascade_name = "haarcascade_eye_tree_eyeglasses.xml";
-   CascadeClassifier face_cascade;
-   CascadeClassifier eyes_cascade;
-   string window_name = "Capture - Face detection";
-   RNG rng(12345);
+    /** Global variables */
+    String face_cascade_name = "haarcascade_frontalface_alt.xml";
+    String eyes_cascade_name = "haarcascade_eye_tree_eyeglasses.xml";
+    CascadeClassifier face_cascade;
+    CascadeClassifier eyes_cascade;
+    String window_name = "Capture - Face detection";
 
-   /** @function main */
-   int main( int argc, const char** argv )
-   {
-     CvCapture* capture;
-     Mat frame;
-
-     //-- 1. Load the cascades
-     if( !face_cascade.load( face_cascade_name ) ){ printf("--(!)Error loading\n"); return -1; };
-     if( !eyes_cascade.load( eyes_cascade_name ) ){ printf("--(!)Error loading\n"); return -1; };
-
-     //-- 2. Read the video stream
-     capture = cvCaptureFromCAM( -1 );
-     if( capture )
-     {
-       while( true )
-       {
-     frame = cvQueryFrame( capture );
-
-     //-- 3. Apply the classifier to the frame
-         if( !frame.empty() )
-         { detectAndDisplay( frame ); }
-         else
-         { printf(" --(!) No captured frame -- Break!"); break; }
-
-         int c = waitKey(10);
-         if( (char)c == 'c' ) { break; }
-        }
-     }
-     return 0;
-   }
-
-  /** @function detectAndDisplay */
-  void detectAndDisplay( Mat frame )
-  {
-    std::vector<Rect> faces;
-    Mat frame_gray;
-
-    cvtColor( frame, frame_gray, CV_BGR2GRAY );
-    equalizeHist( frame_gray, frame_gray );
-
-    //-- Detect faces
-    face_cascade.detectMultiScale( frame_gray, faces, 1.1, 2, 0|CV_HAAR_SCALE_IMAGE, Size(30, 30) );
-
-    for( int i = 0; i < faces.size(); i++ )
+    /** @function main */
+    int main( void )
     {
-      Point center( faces[i].x + faces[i].width*0.5, faces[i].y + faces[i].height*0.5 );
-      ellipse( frame, center, Size( faces[i].width*0.5, faces[i].height*0.5), 0, 0, 360, Scalar( 255, 0, 255 ), 4, 8, 0 );
+        VideoCapture capture;
+        Mat frame;
 
-      Mat faceROI = frame_gray( faces[i] );
-      std::vector<Rect> eyes;
+        //-- 1. Load the cascades
+        if( !face_cascade.load( face_cascade_name ) ){ printf("--(!)Error loading face cascade\n"); return -1; };
+        if( !eyes_cascade.load( eyes_cascade_name ) ){ printf("--(!)Error loading eyes cascade\n"); return -1; };
 
-      //-- In each face, detect eyes
-      eyes_cascade.detectMultiScale( faceROI, eyes, 1.1, 2, 0 |CV_HAAR_SCALE_IMAGE, Size(30, 30) );
+        //-- 2. Read the video stream
+        capture.open( -1 );
+        if ( ! capture.isOpened() ) { printf("--(!)Error opening video capture\n"); return -1; }
 
-      for( int j = 0; j < eyes.size(); j++ )
-       {
-         Point center( faces[i].x + eyes[j].x + eyes[j].width*0.5, faces[i].y + eyes[j].y + eyes[j].height*0.5 );
-         int radius = cvRound( (eyes[j].width + eyes[j].height)*0.25 );
-         circle( frame, center, radius, Scalar( 255, 0, 0 ), 4, 8, 0 );
-       }
+        while (  capture.read(frame) )
+        {
+            if( frame.empty() )
+            {
+                printf(" --(!) No captured frame -- Break!");
+                break;
+            }
+
+            //-- 3. Apply the classifier to the frame
+            detectAndDisplay( frame );
+
+            int c = waitKey(10);
+            if( (char)c == 27 ) { break; } // escape
+        }
+        return 0;
+    }
+
+    /** @function detectAndDisplay */
+    void detectAndDisplay( Mat frame )
+    {
+        std::vector<Rect> faces;
+        Mat frame_gray;
+
+        cvtColor( frame, frame_gray, COLOR_BGR2GRAY );
+        equalizeHist( frame_gray, frame_gray );
+
+        //-- Detect faces
+        face_cascade.detectMultiScale( frame_gray, faces, 1.1, 2, 0|CASCADE_SCALE_IMAGE, Size(30, 30) );
+
+        for( size_t i = 0; i < faces.size(); i++ )
+        {
+            Point center( faces[i].x + faces[i].width/2, faces[i].y + faces[i].height/2 );
+            ellipse( frame, center, Size( faces[i].width/2, faces[i].height/2), 0, 0, 360, Scalar( 255, 0, 255 ), 4, 8, 0 );
+
+            Mat faceROI = frame_gray( faces[i] );
+            std::vector<Rect> eyes;
+
+            //-- In each face, detect eyes
+            eyes_cascade.detectMultiScale( faceROI, eyes, 1.1, 2, 0 |CASCADE_SCALE_IMAGE, Size(30, 30) );
+
+            for( size_t j = 0; j < eyes.size(); j++ )
+            {
+                Point eye_center( faces[i].x + eyes[j].x + eyes[j].width/2, faces[i].y + eyes[j].y + eyes[j].height/2 );
+                int radius = cvRound( (eyes[j].width + eyes[j].height)*0.25 );
+                circle( frame, eye_center, radius, Scalar( 255, 0, 0 ), 4, 8, 0 );
+            }
+        }
+        //-- Show what you got
+        imshow( window_name, frame );
     }
-    //-- Show what you got
-    imshow( window_name, frame );
-   }
 
 Explanation
 ============
@@ -131,4 +130,3 @@ Result
    .. image:: images/Cascade_Classifier_Tutorial_Result_LBP.jpg
       :align: center
       :height: 300pt
-

doc/tutorials/tutorials.rst

@@ -171,17 +171,17 @@ As always, we would be happy to hear your comments and receive your contribution
                  :width:  80pt
                  :alt: gpu icon
 
-*  :ref:`Table-Of-Content-Contrib`
+*  :ref:`Table-Of-Content-Bioinspired`
 
    .. tabularcolumns:: m{100pt} m{300pt}
    .. cssclass:: toctableopencv
 
-   =========== =======================================================
-   |Contrib|       Discover additional contribution to OpenCV.
+   ============= =======================================================
+   |Bioinspired|       Algorithms inspired from biological models.
 
-   =========== =======================================================
+   ============= =======================================================
 
-   .. |Contrib| image:: images/retina.jpg
+   .. |Bioinspired| image:: images/retina.jpg
                  :height: 80pt
                  :width:  80pt
                  :alt: gpu icon
@@ -235,6 +235,6 @@ As always, we would be happy to hear your comments and receive your contribution
    ml/table_of_content_ml/table_of_content_ml
    photo/table_of_content_photo/table_of_content_photo
    gpu/table_of_content_gpu/table_of_content_gpu
-   contrib/table_of_content_contrib/table_of_content_contrib
+   bioinspired/table_of_content_bioinspired/table_of_content_bioinspired
    ios/table_of_content_ios/table_of_content_ios
    general/table_of_content_general/table_of_content_general