scbc_repos/cv/include/opencv2/bioinspired/transientareassegmentationmodule.hpp

/*#******************************************************************************
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 ** copy or use the software.
 **
 **
 ** bioinspired : interfaces allowing OpenCV users to integrate Human Vision System models.
 ** TransientAreasSegmentationModule Use: extract areas that present spatio-temporal changes.
 ** => It should be used at the output of the cv::bioinspired::Retina::getMagnoRAW() output that enhances spatio-temporal changes
 **
 ** Maintainers : Listic lab (code author current affiliation & applications)
 **
 **  Creation - enhancement process 2007-2015
 **      Author: Alexandre Benoit (benoit.alexandre.vision@gmail.com), LISTIC lab, Annecy le vieux, France
 **
 ** Theses algorithm have been developped by Alexandre BENOIT since his thesis with Alice Caplier at Gipsa-Lab (www.gipsa-lab.inpg.fr) and the research he pursues at LISTIC Lab (www.listic.univ-savoie.fr).
 ** Refer to the following research paper for more information:
 ** Strat, S.T.; Benoit, A.; Lambert, P., "Retina enhanced bag of words descriptors for video classification," Signal Processing Conference (EUSIPCO), 2014 Proceedings of the 22nd European , vol., no., pp.1307,1311, 1-5 Sept. 2014 (http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6952461&isnumber=6951911)
 ** 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
 ** This work have been carried out thanks to Jeanny Herault who's research and great discussions are the basis of all this work, please take a look at his book:
 ** 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.
 **
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 **               For Human Visual System tools (bioinspired)
 ** Copyright (C) 2007-2015, LISTIC Lab, Annecy le Vieux and GIPSA Lab, Grenoble, France, all rights reserved.
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#ifndef SEGMENTATIONMODULE_HPP_
#define SEGMENTATIONMODULE_HPP_

/**
@file
@date 2007-2013
@author Alexandre BENOIT, benoit.alexandre.vision@gmail.com
*/

#include "opencv2/core.hpp" // for all OpenCV core functionalities access, including cv::Exception support

namespace cv
{
namespace bioinspired
{
//! @addtogroup bioinspired
//! @{

/** @brief parameter structure that stores the transient events detector setup parameters
*/
struct SegmentationParameters{ // CV_EXPORTS_W_MAP to export to python native dictionnaries
	// default structure instance construction with default values	
	SegmentationParameters():
	    thresholdON(100),
	    thresholdOFF(100),
	    localEnergy_temporalConstant(0.5),
	    localEnergy_spatialConstant(5),
	    neighborhoodEnergy_temporalConstant(1),
	    neighborhoodEnergy_spatialConstant(15),
	    contextEnergy_temporalConstant(1),
	    contextEnergy_spatialConstant(75){};
	// all properties list
	float thresholdON;
	float thresholdOFF;
	//! the time constant of the first order low pass filter, use it to cut high temporal frequencies (noise or fast motion), unit is frames, typical value is 0.5 frame
	float localEnergy_temporalConstant;
	//! the spatial constant of the first order low pass filter, use it to cut high spatial frequencies (noise or thick contours), unit is pixels, typical value is 5 pixel
	float localEnergy_spatialConstant;
	//! local neighborhood energy filtering parameters : the aim is to get information about the energy neighborhood to perform a center surround energy analysis
	float neighborhoodEnergy_temporalConstant;
	float neighborhoodEnergy_spatialConstant;
	//! context neighborhood energy filtering parameters : the aim is to get information about the energy on a wide neighborhood area to filtered out local effects
	float contextEnergy_temporalConstant;
	float contextEnergy_spatialConstant;
};

/** @brief class which provides a transient/moving areas segmentation module

perform a locally adapted segmentation by using the retina magno input data Based on Alexandre
BENOIT thesis: "Le système visuel humain au secours de la vision par ordinateur"

3 spatio temporal filters are used:
- a first one which filters the noise and local variations of the input motion energy
- a second (more powerfull low pass spatial filter) which gives the neighborhood motion energy the
segmentation consists in the comparison of these both outputs, if the local motion energy is higher
to the neighborhood otion energy, then the area is considered as moving and is segmented
- a stronger third low pass filter helps decision by providing a smooth information about the
"motion context" in a wider area
 */

class CV_EXPORTS_W TransientAreasSegmentationModule: public Algorithm
{
public:


    /** @brief return the sze of the manage input and output images
    */
    CV_WRAP virtual Size getSize()=0;

    /** @brief try to open an XML segmentation parameters file to adjust current segmentation instance setup

    - if the xml file does not exist, then default setup is applied
    - warning, Exceptions are thrown if read XML file is not valid
    @param segmentationParameterFile : the parameters filename
    @param applyDefaultSetupOnFailure : set to true if an error must be thrown on error
     */
    CV_WRAP virtual void setup(String segmentationParameterFile="", const bool applyDefaultSetupOnFailure=true)=0;

    /** @brief try to open an XML segmentation parameters file to adjust current segmentation instance setup

    - if the xml file does not exist, then default setup is applied
    - warning, Exceptions are thrown if read XML file is not valid
    @param fs : the open Filestorage which contains segmentation parameters
    @param applyDefaultSetupOnFailure : set to true if an error must be thrown on error
    */
    virtual void setup(cv::FileStorage &fs, const bool applyDefaultSetupOnFailure=true)=0;

    /** @brief try to open an XML segmentation parameters file to adjust current segmentation instance setup

    - if the xml file does not exist, then default setup is applied
    - warning, Exceptions are thrown if read XML file is not valid
    @param newParameters : a parameters structures updated with the new target configuration
     */
    virtual void setup(SegmentationParameters newParameters)=0;

    /** @brief return the current parameters setup
    */
    virtual SegmentationParameters getParameters()=0;

    /** @brief parameters setup display method
    @return a string which contains formatted parameters information
    */
    CV_WRAP virtual const String printSetup()=0;

    /** @brief write xml/yml formated parameters information
    @param fs : the filename of the xml file that will be open and writen with formatted parameters information
    */
    CV_WRAP virtual void write( String fs ) const=0;

    /** @brief write xml/yml formated parameters information
    @param fs : a cv::Filestorage object ready to be filled
    */
    virtual void write( cv::FileStorage& fs ) const=0;

    /** @brief main processing method, get result using methods getSegmentationPicture()
    @param inputToSegment : the image to process, it must match the instance buffer size !
    @param channelIndex : the channel to process in case of multichannel images
    */
    CV_WRAP virtual void run(InputArray inputToSegment, const int channelIndex=0)=0;

    /** @brief access function
    @return the last segmentation result: a boolean picture which is resampled between 0 and 255 for a display purpose
   */
    CV_WRAP virtual void getSegmentationPicture(OutputArray transientAreas)=0;

    /** @brief cleans all the buffers of the instance
    */
    CV_WRAP virtual void clearAllBuffers()=0;

    /** @brief allocator
    @param inputSize : size of the images input to segment (output will be the same size)
     */
    CV_WRAP static Ptr<TransientAreasSegmentationModule> create(Size inputSize);
};

//! @}

}} // namespaces end : cv and bioinspired


#endif