Fuzzy module added

fixes

fixed compile error and warning

Remove AGAST (for merger to upstream opencv)

wrapping remaining xfeature2d classes to scripting

added test for http://code.opencv.org/issues/3943;
replaced "const InputArray" with "InputArray" to avoid warnings about "const const _InputArray&"

added test for http://code.opencv.org/issues/3943;
replaced "const InputArray" with "InputArray" to avoid warnings about "const const _InputArray&"

adding LATCH

fixed warnings in LATCH

fixed errors

fixed warnings

fixed warnings2

fixed warnings3

fixed warnings4

fixed warnings5

added description of LATCH and fixed indentation

cleaned the code a bit

added tests and renamed LATCH

Code to grab the red lined polygon from Google Maps

added python support and completed documentation

figure update

added stdout welcome message and fixed warning

fixed warning

text enhancement

trying to fix python wrapper warning on win64

trying to solve pyhton warnings

bugfix4269 included remarks in http://answers.opencv.org/question/59293/problem-with-example-motemplcpp/

Replace tab with four space

Add rotation invariance option for BRIEF descriptor.

Fix docs and repush for buildbot.

Bug fix for feature extraction

According to CartToPolar() function documentation, result angles could be in range (0..360). To prevent index overflow this check is important.

Adding edge-aware disparity filtering

Added basic interface and demo for disparity filtering, added unoptimized fast weighted least
squares filter implementation. Current demo tests domain transform, guided and
weighted least squares filters on a dataset, measures speed and quality.

Fix for Bug 4074. This seems to be just a typo-error, because the Tesseract API can handle correctly with RGB images (double-checked and it works).

Fix for Bug opencv#3633: do away with "quads [2][3] = 255;" The four lowest bits in each quads[i][j] correspond to the 2x2 binary patterns Q_1, Q_2, Q_3 in the NM paper [1] (see in page 4 at the end of first column). Q_1 and Q_2 have four patterns, while Q_3 has only two.

added INRIA pedestrian dataset

autowbGrayworld: include+src+test+testdata+sample

Add saturation based thresholding to grayworld WB

Add basic perf tests for grayworld

Add more doxygen comments

Suppress uchar conv related warning on Windows

Apply fixes suggested by Vadim

Be more correct with int types

Remove dangling N_good++

Use cvRound to suppress Windows warnings

remove floor call

vs2010 does not know, ceil, floor, round and friends.

also, those are plain integer divisions, that do not need floor at all.

New stereo module created and added some relevant files for this module

Update README.md

made some extra changes to the modules so I receive no warnings

moved the opencv2/core/private.hpp from stereo_binary_sgbm.cpp to precomp.hpp

fix for issue opencv#195

avoid overflow in histogram access

SurfaceMatching: OpenMP indices

Fixes compiler error: "index variable in OpenMP 'for' statement must have signed integral type"

Adding confidence support and optimizing disparity filtering

DisparityWLSFilter demonstrated the best results, so I removed all the other
filters. Quality was significantly improved by adding confidence support
to the filter (left-right consistency + penalty for areas near depth discontinuities).
Filter was optimized using parallel_for_ and HAL intrinsics. Demo application was
rewritten for better compliance with OpenCV standards. Added accuracy and
performance tests. Documentation was added, as well as references to the
original papers.

added PASCAL VOC dataset

+ add KCF Tracker, initial commit, added: tutorial, trackerKCF.cpp, modified: tracker.cpp, tracker.hpp

adding the resize feature

References for KCF tracker and KCF-CN  tracker

Unified the formatting

Fixed: ROI extraction when the given ROI is out of image; made the max_patch_size to be adjustable; add the CN feature extraction method

Removing all shadowing variables, make functions to const, make the table of color-names become static

change the color-names table to const

Add a framework for choosing the descriptor

Added error message for descriptor other than GRAY

Added new line at end of file

Fixing the ColorNames table initialization

Fixed warning: conversion from double to int

Updated the support for color-names features and fixing some typos

Fixing the tabulation

Split the training coefficient into numerator and denumerator

Added the feature compression method

Fixing some indentations

Fixing some indentations

Fixing alignments

Fixing some alignments

Use Doxigen format

Remove whitespaces

Removing whitespaces in featureColorName.cpp

Add an example code for the KCF tracker

update the header in example/kcf.cpp

Updating the rectangle drawing, avoid warning from variable conversion

Added doxygen documentations

Fixing warnings

remove warnings

Fixing some warnings

TLD Fixes & Optimizations

1. TLD now have module structure
2. Made some small code optimizations
3. Fixed Ensemble Classifier according to the original paper - 10
randomized ferns
4. Added comments to most of the functions and methods

Added test on TLD Dataset

Added BSD-compatible license

Added BSD-compatible license to some files

Fixed header

Fixed build error

Fix

Fix opencv#2

Fix opencv#3

Fix opencv#4

Fixed Warnings opencv#1

Fixed Warnings opencv#2

Fixed Warnings opencv#3

Shadow Fix

Fixing whitespaces

Fixing whitespaces opencv#2

Fixing whitespaces opencv#3

Adds a first implementation of the OCRBeamSearchDecoder class using the Single Layer CNN character classifier described in Coates, Adam, et al. paper: Text detection and character recognition in scene images with unsupervised feature learning, ICDAR 2011

Add a demo program for the OCRBeamSearchDecoder class and needed data files

trailing whitespaces

fix compilation warnings

fix win64 compilation error: arrays must be defined with compile-time fixed size :)

fix doxygen warnings

Fix for opencv#278 - core dump in the case of no match results.

Modified reported poses by constraining to the number of poses found.

ulong -> size_t

fixed warnings in the tracking module

Added OCL versions of Sr and Sc functions

2-nd level of parallelization + detector remake

1. Added 2-nd level of parallelization of NN on OpenCL
2. Restructured detector - now all filters work independently:
Variance Filter->Ensemble->NN,  through "buffers"

Warnings Fix opencv#1

Fixing Warnings opencv#2

Fixing Warnings opencv#3

Fixing Warnings opencv#4

Fixing Warnings opencv#5

Fixing Warnings opencv#3

Fixing Warnings opencv#4

Fixing Warnings opencv#5

Added OCL version of "integrateAdditional" function

Whitespace Fix

Transparent API Support

Fixing Warnings

Fixed bug in LSDDetector where mask doesn't remove all undesired lines

Fixing GCC 4.9 warning

Fix memory leak bug #4420

Fix bug #4373: Error (Assertion failed in resize) when passing very elongated contours to the recognition module

make sources compile again on MSVC 2012 (VC 11) by adding round()

fix suffix that was in-compatible with MSVC 2012 (VC 11)

eliminate some warnings

use better condition for checking if compiler supports round()

fixing facerecognizer tutorials and interface

Adds createOCRHMMTransitionsTable() utility function to create a tailored language model transitions table from a given list of words (lexicon)

update to use the new createOCRHMMTransitionsTable() function, and fix program description in header comments

fix Winx64 warnings

Better CNN model for character recognition. Trained with an augmented dataset by adding translation/scale variations. Updated the croped word recognition with new class numbering (compatible with previous NM classifier).

Overload the run() method in BaseOCR class in order to adapt to different classifier callbacks. The original run() method accepts only one Mat input image, this is expected to be a binarzed image with black and white text and works both with the OCRTesseract class and the OCRHMMDecoder class when the character classifier callback works with binary images (e.g. NM). The new run() method accepts two Mat input parameters. One for the gray scale (or color) source image and the other for a binary mask where each connected component corresponds to a pre-segmented character in the input image. This way the OCRHMMDecoder is able to work with character classifiers that operate in grey scale (or color) images (e.g. a CNN).

Adds example on segmented word recognition. Shows the use of the OCRHMMDecoder with the NM and CNN default classifiers.

Minor bugfix: removes unwanted space character at the begining of recognition output strings.

Fix w64 warnings

Fix w64 warnings

Improving DisparityWLSFilter interface and adding a tutorial

Now the filter natively supports StereoBM and StereoSGBM with no
parameter tuning required. Also, now user won't need to set the ROI and
the right matcher parameters manually, it is all done in the respective
convenience factory method based on the left matcher instance. Tutorial
was added to clarify the provided example of use.

doc update

Author: tucna

modules/README.md

@@ -51,3 +51,7 @@ $ cmake -D OPENCV_EXTRA_MODULES_PATH=<opencv_contrib>/modules -D BUILD_opencv_re
 21. **opencv_xobjdetect**: Integral Channel Features Detector Framework.
 
 22. **opencv_xphoto**: Additional photo processing algorithms: Color balance / Denoising / Inpainting.
+
+23. **opencv_stereo**: Stereo Correspondence done with different descriptors: Census / CS-Census / MCT / BRIEF / MV / RT.
+
+24. **opencv_fuzzy**: New module focused on the fuzzy image processing.

modules/bioinspired/CMakeLists.txt

@@ -1,3 +1,3 @@
 set(the_description "Biologically inspired algorithms")
 ocv_warnings_disable(CMAKE_CXX_FLAGS -Wundef)
-ocv_define_module(bioinspired opencv_core OPTIONAL opencv_highgui opencv_ocl WRAP java)
+ocv_define_module(bioinspired opencv_core OPTIONAL opencv_highgui opencv_ocl WRAP java python)

modules/bioinspired/README.md

@@ -1,2 +1,6 @@
 Biologically inspired vision models and derivated tools
 =======================================================
+
+1. A biological retina model for image spatio-temporal noise and luminance changes enhancement
+2. A transient areas (spatio-temporal events) segmentation tool to use at the output of the Retina
+3. High Dynamic Range (HDR >8bit images) tone mapping to (conversion to 8bit) use cas of the retina  

modules/bioinspired/doc/bioinspired.bib

@@ -9,6 +9,15 @@ @article{Benoit2010
   publisher={Elsevier}
 }
 
+@INPROCEEDINGS{Benoit2014,
+  author={Strat, S.T. and Benoit, A. and Lambert, P.}, 
+  booktitle={Signal Processing Conference (EUSIPCO), 2014 Proceedings of the 22nd European}, 
+  title={Retina enhanced bag of words descriptors for video classification}, 
+  year={2014}, 
+  month={Sept}, 
+  pages={1307-1311}
+}
+
 @inproceedings{Strat2013,
   title={Retina enhanced SIFT descriptors for video indexing},
   author={Strat, Sabin Tiberius and Benoit, Alexandre and Lambert, Patrick},

modules/bioinspired/doc/images/HDRtoneMapping_candleSample.jpg

@@ -12,12 +12,12 @@
  **
  ** Maintainers : Listic lab (code author current affiliation & applications)
  **
- **  Creation - enhancement process 2007-2013
+ **  Creation - enhancement process 2007-2015
  **      Author: Alexandre Benoit ([email protected]), 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. , Caplier A., "Retina Enhanced SURF Descriptors for Spatio-Temporal Concept Detection", Multimedia Tools and Applications, 2012 (DOI: 10.1007/s11042-012-1280-0)
+ ** 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.
@@ -30,7 +30,7 @@
  ** Copyright (C) 2008-2011, Willow Garage Inc., all rights reserved.
  **
  **               For Human Visual System tools (bioinspired)
- ** Copyright (C) 2007-2011, LISTIC Lab, Annecy le Vieux and GIPSA Lab, Grenoble, France, all rights reserved.
+ ** Copyright (C) 2007-2015, LISTIC Lab, Annecy le Vieux and GIPSA Lab, Grenoble, France, all rights reserved.
  **
  ** Third party copyrights are property of their respective owners.
  **
@@ -74,10 +74,37 @@ 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
@@ -96,30 +123,6 @@ class CV_EXPORTS_W TransientAreasSegmentationModule: public Algorithm
 {
 public:
 
-    //! parameters structure
-    struct CV_EXPORTS_W SegmentationParameters{
-        SegmentationParameters():
-            thresholdON(100),
-            thresholdOFF(100),
-            localEnergy_temporalConstant(0.5),
-            localEnergy_spatialConstant(5),
-            neighborhoodEnergy_temporalConstant(1),
-            neighborhoodEnergy_spatialConstant(15),
-            contextEnergy_temporalConstant(1),
-            contextEnergy_spatialConstant(75){};// default setup
-        CV_PROP_RW float thresholdON;
-        CV_PROP_RW 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
-        CV_PROP_RW 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
-        CV_PROP_RW 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
-        CV_PROP_RW float neighborhoodEnergy_temporalConstant;
-        CV_PROP_RW 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
-        CV_PROP_RW float contextEnergy_temporalConstant;
-        CV_PROP_RW float contextEnergy_spatialConstant;
-    };
 
     /** @brief return the sze of the manage input and output images
     */
@@ -141,19 +144,19 @@ class CV_EXPORTS_W TransientAreasSegmentationModule: public Algorithm
     @param fs : the open Filestorage which contains segmentation parameters
     @param applyDefaultSetupOnFailure : set to true if an error must be thrown on error
     */
-    CV_WRAP virtual void setup(cv::FileStorage &fs, const bool applyDefaultSetupOnFailure=true)=0;
+    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
      */
-    CV_WRAP virtual void setup(SegmentationParameters newParameters)=0;
+    virtual void setup(SegmentationParameters newParameters)=0;
 
     /** @brief return the current parameters setup
     */
-    CV_WRAP virtual SegmentationParameters getParameters()=0;
+    virtual SegmentationParameters getParameters()=0;
 
     /** @brief parameters setup display method
     @return a string which contains formatted parameters information
@@ -168,7 +171,7 @@ class CV_EXPORTS_W TransientAreasSegmentationModule: public Algorithm
     /** @brief write xml/yml formated parameters information
     @param fs : a cv::Filestorage object ready to be filled
     */
-    CV_WRAP virtual void write( cv::FileStorage& fs ) const=0;
+    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 !

modules/bioinspired/doc/images/HDRtoneMapping_candleSample.svg

@@ -0,0 +1,91 @@
+//============================================================================
+// Name        : retinademo.cpp
+// Author      : Alexandre Benoit, [email protected]
+// Version     : 0.1
+// Copyright   : LISTIC/GIPSA French Labs, May 2015
+// Description : Gipsa/LISTIC Labs quick retina demo in C++, Ansi-style
+//============================================================================
+
+// include bioinspired module and OpenCV core utilities
+#include "opencv2/bioinspired.hpp"
+#include "opencv2/imgcodecs.hpp"
+#include "opencv2/videoio.hpp"
+#include "opencv2/highgui.hpp"
+#include <iostream>
+#include <cstring>
+
+// main function
+int main(int argc, char* argv[]) {
+
+  // basic input arguments checking
+  if (argc>1)
+  {
+        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 retina model allows spatio-temporal image processing (applied on a webcam sequences)."<<std::endl;
+	std::cout<<"* As a summary, these are the retina model properties:"<<std::endl;
+	std::cout<<"* => It applies a spectral whithening (mid-frequency details enhancement)"<<std::endl;
+	std::cout<<"* => high frequency spatio-temporal noise reduction"<<std::endl;
+	std::cout<<"* => low frequency luminance to be reduced (luminance range compression)"<<std::endl;
+	std::cout<<"* => local logarithmic luminance compression allows details to be enhanced in low light conditions\n"<<std::endl;
+	std::cout<<"* for more information, reer to the following papers :"<<std::endl;
+	std::cout<<"* 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"<<std::endl;
+	std::cout<<"* 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."<<std::endl;
+	std::cout<<"* => reports comments/remarks at [email protected]"<<std::endl;
+	std::cout<<"* => more informations and papers at : http://sites.google.com/site/benoitalexandrevision/"<<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;
+       
+        if (strcmp(argv[1],  "help")==0){
+	    std::cout<<"No help provided for now, please test the retina Demo for a more complete program"<<std::endl;
+        }
+  }
+
+  std::string inputMediaType=argv[1];
+  // declare the retina input buffer.
+  cv::Mat inputFrame;
+  // setup webcam reader and grab a first frame to get its size
+  cv::VideoCapture videoCapture(0); 
+  videoCapture>>inputFrame;
+
+  // allocate a retina instance with input size equal to the one of the loaded image
+  cv::Ptr<cv::bioinspired::Retina> myRetina = cv::bioinspired::createRetina(inputFrame.size());
+
+  /* retina parameters management methods use sample
+     -> save current (here default) retina parameters to a xml file (you may use it only one time to get the file and modify it)
+  */
+  myRetina->write("RetinaDefaultParameters.xml");
+
+  // -> load parameters if file exists
+  myRetina->setup("RetinaSpecificParameters.xml");
+
+  // reset all retina buffers (open your eyes)  
+  myRetina->clearBuffers();
+
+  // declare retina output buffers
+  cv::Mat retinaOutput_parvo;
+  cv::Mat retinaOutput_magno;
+
+  //main processing loop
+  bool stillProcess=true;
+  while(stillProcess){
+	
+	// if using video stream, then, grabbing a new frame, else, input remains the same
+	if (videoCapture.isOpened())
+		videoCapture>>inputFrame;
+        else
+		stillProcess=false;
+	// run retina filter
+	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(5);
+  }
+
+}