Merge branch 'gsoc' of https://github.com/Bellaktris/opencv_contrib into gsoc

modules/adas/CMakeLists.txt

+set(the_description "Automatic driver assistance algorithms")
+ocv_define_module(adas opencv_xobjdetect)
+
+add_subdirectory(tools)

modules/adas/include/opencv2/adas.hpp

+/*
+
+By downloading, copying, installing or using the software you agree to this
+license. If you do not agree to this license, do not download, install,
+copy or use the software.
+
+
+                          License Agreement
+               For Open Source Computer Vision Library
+                       (3-clause BSD License)
+
+Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+Third party copyrights are property of their respective owners.
+
+Redistribution and use in source and binary forms, with or without modification,
+are permitted provided that the following conditions are met:
+
+  * Redistributions of source code must retain the above copyright notice,
+    this list of conditions and the following disclaimer.
+
+  * Redistributions in binary form must reproduce the above copyright notice,
+    this list of conditions and the following disclaimer in the documentation
+    and/or other materials provided with the distribution.
+
+  * Neither the names of the copyright holders nor the names of the contributors
+    may be used to endorse or promote products derived from this software
+    without specific prior written permission.
+
+This software is provided by the copyright holders and contributors "as is" and
+any express or implied warranties, including, but not limited to, the implied
+warranties of merchantability and fitness for a particular purpose are
+disclaimed. In no event shall copyright holders or contributors be liable for
+any direct, indirect, incidental, special, exemplary, or consequential damages
+(including, but not limited to, procurement of substitute goods or services;
+loss of use, data, or profits; or business interruption) however caused
+and on any theory of liability, whether in contract, strict liability,
+or tort (including negligence or otherwise) arising in any way out of
+the use of this software, even if advised of the possibility of such damage.
+
+*/

modules/adas/tools/CMakeLists.txt

+add_subdirectory(fcw_train)
+add_subdirectory(fcw_detect)

modules/adas/tools/fcw_detect/CMakeLists.txt

+set(name fcw_detect)
+set(the_target opencv_${name})
+
+set(OPENCV_${the_target}_DEPS opencv_core opencv_imgcodecs opencv_videoio
+    opencv_highgui opencv_xobjdetect)
+
+ocv_check_dependencies(${OPENCV_${the_target}_DEPS})
+
+if(NOT OCV_DEPENDENCIES_FOUND)
+  return()
+endif()
+
+project(${the_target})
+
+ocv_include_directories("${OpenCV_SOURCE_DIR}/include/opencv")
+ocv_include_modules(${OPENCV_${the_target}_DEPS})
+
+file(GLOB ${the_target}_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp)
+
+add_executable(${the_target} ${${the_target}_SOURCES})
+
+target_link_libraries(${the_target} ${OPENCV_${the_target}_DEPS})
+
+set_target_properties(${the_target} PROPERTIES
+                      DEBUG_POSTFIX "${OPENCV_DEBUG_POSTFIX}"
+                      ARCHIVE_OUTPUT_DIRECTORY ${LIBRARY_OUTPUT_PATH}
+                      RUNTIME_OUTPUT_DIRECTORY ${EXECUTABLE_OUTPUT_PATH}
+                      INSTALL_NAME_DIR lib
+                      OUTPUT_NAME ${the_target})
+
+if(ENABLE_SOLUTION_FOLDERS)
+  set_target_properties(${the_target} PROPERTIES FOLDER "applications")
+endif()
+
+install(TARGETS ${the_target} RUNTIME DESTINATION bin COMPONENT main)

modules/adas/tools/fcw_detect/fcw_detect.cpp

+#include <string>
+using std::string;
+
+#include <vector>
+using std::vector;
+
+#include <iostream>
+using std::cerr;
+using std::endl;
+
+#include <opencv2/core.hpp>
+using cv::Rect;
+using cv::Size;
+using cv::Mat;
+using cv::Mat_;
+using cv::Vec3b;
+
+#include <opencv2/highgui.hpp>
+using cv::imread;
+using cv::imwrite;
+
+#include <opencv2/core/utility.hpp>
+using cv::CommandLineParser;
+using cv::FileStorage;
+
+#include <opencv2/xobjdetect.hpp>
+using cv::xobjdetect::ICFDetector;
+
+static Mat visualize(const Mat &image, const vector<Rect> &objects)
+{
+    CV_Assert(image.type() == CV_8UC3);
+    Mat_<Vec3b> img = image.clone();
+    for( size_t j = 0; j < objects.size(); ++j )
+    {
+        Rect obj = objects[j];
+        int x = obj.x;
+        int y = obj.y;
+        int width = obj.width;
+        int height = obj.height;
+        for( int i = y; i <= y + height; ++i ) {
+            img(i, x) = Vec3b(255, 0, 0);
+            img(i, x + width) = Vec3b(255, 0, 0);
+        }
+        for( int i = x; i <= x + width; ++i) {
+            img(y, i) = Vec3b(255, 0, 0);
+            img(y + height, i) = Vec3b(255, 0, 0);
+        }
+    }
+    return img;
+}
+
+int main(int argc, char *argv[])
+{
+    const string keys =
+        "{help           |           | print this message}"
+        "{model_filename | model.xml | filename for reading model}"
+        "{image_path     |  test.png | path to image for detection}"
+        "{out_image_path |   out.png | path to image for output}"
+        "{threshold      |       0.0 | threshold for cascade}"
+        ;
+
+    CommandLineParser parser(argc, argv, keys);
+    parser.about("FCW detection");
+
+    if( parser.has("help") || argc == 1)
+    {
+        parser.printMessage();
+        return 0;
+    }
+
+    string model_filename = parser.get<string>("model_filename");
+    string image_path = parser.get<string>("image_path");
+    string out_image_path = parser.get<string>("out_image_path");
+    float threshold = parser.get<float>("threshold");
+
+
+    if( !parser.check() )
+    {
+        parser.printErrors();
+        return 1;
+    }
+
+    ICFDetector detector;
+    FileStorage fs(model_filename, FileStorage::READ);
+    detector.read(fs["icfdetector"]);
+    fs.release();
+    vector<Rect> objects;
+    Mat img = imread(image_path);
+    detector.detect(img, objects, 1.1f, Size(40, 40),
+        Size(300, 300), threshold);
+    imwrite(out_image_path, visualize(img, objects));
+}

modules/adas/tools/fcw_train/CMakeLists.txt

+set(name fcw_train)
+set(the_target opencv_${name})
+
+set(OPENCV_${the_target}_DEPS opencv_core opencv_imgcodecs opencv_videoio
+    opencv_highgui opencv_xobjdetect)
+
+ocv_check_dependencies(${OPENCV_${the_target}_DEPS})
+
+if(NOT OCV_DEPENDENCIES_FOUND)
+  return()
+endif()
+
+project(${the_target})
+
+ocv_include_directories("${OpenCV_SOURCE_DIR}/include/opencv")
+ocv_include_modules(${OPENCV_${the_target}_DEPS})
+
+file(GLOB ${the_target}_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp)
+
+add_executable(${the_target} ${${the_target}_SOURCES})
+
+target_link_libraries(${the_target} ${OPENCV_${the_target}_DEPS})
+
+set_target_properties(${the_target} PROPERTIES
+                      DEBUG_POSTFIX "${OPENCV_DEBUG_POSTFIX}"
+                      ARCHIVE_OUTPUT_DIRECTORY ${LIBRARY_OUTPUT_PATH}
+                      RUNTIME_OUTPUT_DIRECTORY ${EXECUTABLE_OUTPUT_PATH}
+                      INSTALL_NAME_DIR lib
+                      OUTPUT_NAME ${the_target})
+
+if(ENABLE_SOLUTION_FOLDERS)
+  set_target_properties(${the_target} PROPERTIES FOLDER "applications")
+endif()
+
+install(TARGETS ${the_target} RUNTIME DESTINATION bin COMPONENT main)

modules/adas/tools/fcw_train/fcw_train.cpp

+#include <cstdio>
+#include <cstring>
+
+#include <string>
+using std::string;
+
+#include <vector>
+using std::vector;
+
+#include <fstream>
+using std::ifstream;
+using std::getline;
+
+#include <sstream>
+using std::stringstream;
+
+#include <iostream>
+using std::cerr;
+using std::endl;
+
+#include <opencv2/core.hpp>
+using cv::Rect;
+using cv::Size;
+#include <opencv2/highgui.hpp>
+using cv::imread;
+#include <opencv2/core/utility.hpp>
+using cv::CommandLineParser;
+using cv::FileStorage;
+
+#include <opencv2/xobjdetect.hpp>
+
+using cv::xobjdetect::ICFDetectorParams;
+using cv::xobjdetect::ICFDetector;
+using cv::xobjdetect::WaldBoost;
+using cv::xobjdetect::WaldBoostParams;
+using cv::Mat;
+
+static bool read_model_size(const char *str, int *rows, int *cols)
+{
+    int pos = 0;
+    if( sscanf(str, "%dx%d%n", rows, cols, &pos) != 2 || str[pos] != '\0' ||
+        *rows <= 0 || *cols <= 0)
+    {
+        return false;
+    }
+    return true;
+}
+
+int main(int argc, char *argv[])
+{
+    const string keys =
+        "{help           |           | print this message}"
+        "{pos_path       |       pos | path to training object samples}"
+        "{bg_path        |        bg | path to background images}"
+        "{bg_per_image   |         5 | number of windows to sample per bg image}"
+        "{feature_count  |     10000 | number of features to generate}"
+        "{weak_count     |       100 | number of weak classifiers in cascade}"
+        "{model_size     |     40x40 | model size in pixels}"
+        "{model_filename | model.xml | filename for saving model}"
+        ;
+
+    CommandLineParser parser(argc, argv, keys);
+    parser.about("FCW trainer");
+
+    if( parser.has("help") || argc == 1)
+    {
+        parser.printMessage();
+        return 0;
+    }
+
+    string pos_path = parser.get<string>("pos_path");
+    string bg_path = parser.get<string>("bg_path");
+    string model_filename = parser.get<string>("model_filename");
+
+    ICFDetectorParams params;
+    params.feature_count = parser.get<int>("feature_count");
+    params.weak_count = parser.get<int>("weak_count");
+    params.bg_per_image = parser.get<int>("bg_per_image");
+
+    string model_size = parser.get<string>("model_size");
+    if( !read_model_size(model_size.c_str(), &params.model_n_rows,
+        &params.model_n_cols) )
+    {
+        cerr << "Error reading model size from `" << model_size << "`" << endl;
+        return 1;
+    }
+
+    if( params.feature_count <= 0 )
+    {
+        cerr << "feature_count must be positive number" << endl;
+        return 1;
+    }
+
+    if( params.weak_count <= 0 )
+    {
+        cerr << "weak_count must be positive number" << endl;
+        return 1;
+    }
+
+    if( params.bg_per_image <= 0 )
+    {
+        cerr << "bg_per_image must be positive number" << endl;
+        return 1;
+    }
+
+    if( !parser.check() )
+    {
+        parser.printErrors();
+        return 1;
+    }
+
+    ICFDetector detector;
+    detector.train(pos_path, bg_path, params);
+    FileStorage fs(model_filename, FileStorage::WRITE);
+    fs << "icfdetector";
+    detector.write(fs);
+    fs.release();
+}

modules/bgsegm/CMakeLists.txt

+set(the_description "Background Segmentation Algorithms")
+ocv_define_module(bgsegm opencv_core opencv_imgproc opencv_video opencv_highgui)

modules/bgsegm/include/opencv2/bgsegm.hpp

+/*
+By downloading, copying, installing or using the software you agree to this
+license. If you do not agree to this license, do not download, install,
+copy or use the software.
+
+
+                          License Agreement
+               For Open Source Computer Vision Library
+                       (3-clause BSD License)
+
+Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+Third party copyrights are property of their respective owners.
+
+Redistribution and use in source and binary forms, with or without modification,
+are permitted provided that the following conditions are met:
+
+  * Redistributions of source code must retain the above copyright notice,
+    this list of conditions and the following disclaimer.
+
+  * Redistributions in binary form must reproduce the above copyright notice,
+    this list of conditions and the following disclaimer in the documentation
+    and/or other materials provided with the distribution.
+
+  * Neither the names of the copyright holders nor the names of the contributors
+    may be used to endorse or promote products derived from this software
+    without specific prior written permission.
+
+This software is provided by the copyright holders and contributors "as is" and
+any express or implied warranties, including, but not limited to, the implied
+warranties of merchantability and fitness for a particular purpose are
+disclaimed. In no event shall copyright holders or contributors be liable for
+any direct, indirect, incidental, special, exemplary, or consequential damages
+(including, but not limited to, procurement of substitute goods or services;
+loss of use, data, or profits; or business interruption) however caused
+and on any theory of liability, whether in contract, strict liability,
+or tort (including negligence or otherwise) arising in any way out of
+the use of this software, even if advised of the possibility of such damage.
+*/
+
+#ifndef __OPENCV_BGSEGM_HPP__
+#define __OPENCV_BGSEGM_HPP__
+
+#include "opencv2/video.hpp"
+
+#ifdef __cplusplus
+
+namespace cv
+{
+namespace bgsegm
+{
+
+/*!
+ Gaussian Mixture-based Backbround/Foreground Segmentation Algorithm
+
+ The class implements the following algorithm:
+ "An improved adaptive background mixture model for real-time tracking with shadow detection"
+ P. KadewTraKuPong and R. Bowden,
+ Proc. 2nd European Workshp on Advanced Video-Based Surveillance Systems, 2001."
+ http://personal.ee.surrey.ac.uk/Personal/R.Bowden/publications/avbs01/avbs01.pdf
+
+*/
+class CV_EXPORTS_W BackgroundSubtractorMOG : public BackgroundSubtractor
+{
+public:
+    CV_WRAP virtual int getHistory() const = 0;
+    CV_WRAP virtual void setHistory(int nframes) = 0;
+
+    CV_WRAP virtual int getNMixtures() const = 0;
+    CV_WRAP virtual void setNMixtures(int nmix) = 0;
+
+    CV_WRAP virtual double getBackgroundRatio() const = 0;
+    CV_WRAP virtual void setBackgroundRatio(double backgroundRatio) = 0;
+
+    CV_WRAP virtual double getNoiseSigma() const = 0;
+    CV_WRAP virtual void setNoiseSigma(double noiseSigma) = 0;
+};
+
+CV_EXPORTS_W Ptr<BackgroundSubtractorMOG>
+    createBackgroundSubtractorMOG(int history=200, int nmixtures=5,
+                                  double backgroundRatio=0.7, double noiseSigma=0);
+                                  
+/**
+ * Background Subtractor module. Takes a series of images and returns a sequence of mask (8UC1)
+ * images of the same size, where 255 indicates Foreground and 0 represents Background.
+ * This class implements an algorithm described in "Visual Tracking of Human Visitors under
+ * Variable-Lighting Conditions for a Responsive Audio Art Installation," A. Godbehere,
+ * A. Matsukawa, K. Goldberg, American Control Conference, Montreal, June 2012.
+ */
+class CV_EXPORTS_W BackgroundSubtractorGMG : public BackgroundSubtractor
+{
+public:
+    CV_WRAP virtual int getMaxFeatures() const = 0;
+    CV_WRAP virtual void setMaxFeatures(int maxFeatures) = 0;
+
+    CV_WRAP virtual double getDefaultLearningRate() const = 0;
+    CV_WRAP virtual void setDefaultLearningRate(double lr) = 0;
+
+    CV_WRAP virtual int getNumFrames() const = 0;
+    CV_WRAP virtual void setNumFrames(int nframes) = 0;
+
+    CV_WRAP virtual int getQuantizationLevels() const = 0;
+    CV_WRAP virtual void setQuantizationLevels(int nlevels) = 0;
+
+    CV_WRAP virtual double getBackgroundPrior() const = 0;
+    CV_WRAP virtual void setBackgroundPrior(double bgprior) = 0;
+
+    CV_WRAP virtual int getSmoothingRadius() const = 0;
+    CV_WRAP virtual void setSmoothingRadius(int radius) = 0;
+
+    CV_WRAP virtual double getDecisionThreshold() const = 0;
+    CV_WRAP virtual void setDecisionThreshold(double thresh) = 0;
+
+    CV_WRAP virtual bool getUpdateBackgroundModel() const = 0;
+    CV_WRAP virtual void setUpdateBackgroundModel(bool update) = 0;
+
+    CV_WRAP virtual double getMinVal() const = 0;
+    CV_WRAP virtual void setMinVal(double val) = 0;
+
+    CV_WRAP virtual double getMaxVal() const = 0;
+    CV_WRAP virtual void setMaxVal(double val) = 0;
+};
+
+CV_EXPORTS_W Ptr<BackgroundSubtractorGMG> createBackgroundSubtractorGMG(int initializationFrames=120,
+                                                                        double decisionThreshold=0.8);                                  
+
+}
+}
+
+#endif
+#endif

modules/bgsegm/samples/bgfg_gmg.cpp

+/*
+ * FGBGTest.cpp
+ *
+ *  Created on: May 7, 2012
+ *      Author: Andrew B. Godbehere
+ */
+
+#include "opencv2/bgsegm.hpp"
+#include "opencv2/videoio.hpp"
+#include "opencv2/highgui.hpp"
+#include <opencv2/core/utility.hpp>
+#include <iostream>
+
+using namespace cv;
+using namespace cv::bgsegm;
+
+static void help()
+{
+    std::cout <<
+    "\nA program demonstrating the use and capabilities of a particular BackgroundSubtraction\n"
+    "algorithm described in A. Godbehere, A. Matsukawa, K. Goldberg, \n"
+    "\"Visual Tracking of Human Visitors under Variable-Lighting Conditions for a Responsive\n"
+    "Audio Art Installation\", American Control Conference, 2012, used in an interactive\n"
+    "installation at the Contemporary Jewish Museum in San Francisco, CA from March 31 through\n"
+    "July 31, 2011.\n"
+    "Call:\n"
+    "./BackgroundSubtractorGMG_sample\n"
+    "Using OpenCV version " << CV_VERSION << "\n"<<std::endl;
+}
+
+int main(int argc, char** argv)
+{
+    help();
+
+    setUseOptimized(true);
+    setNumThreads(8);
+
+    Ptr<BackgroundSubtractor> fgbg = createBackgroundSubtractorGMG(20, 0.7);
+    if (!fgbg)
+    {
+        std::cerr << "Failed to create BackgroundSubtractor.GMG Algorithm." << std::endl;
+        return -1;
+    }
+
+    VideoCapture cap;
+    if (argc > 1)
+        cap.open(argv[1]);
+    else
+        cap.open(0);
+
+    if (!cap.isOpened())
+    {
+        std::cerr << "Cannot read video. Try moving video file to sample directory." << std::endl;
+        return -1;
+    }
+
+    Mat frame, fgmask, segm;
+
+    namedWindow("FG Segmentation", WINDOW_NORMAL);
+
+    for (;;)
+    {
+        cap >> frame;
+
+        if (frame.empty())
+            break;
+
+        fgbg->apply(frame, fgmask);
+
+        frame.convertTo(segm, CV_8U, 0.5);
+        add(frame, Scalar(100, 100, 0), segm, fgmask);
+
+        imshow("FG Segmentation", segm);
+
+        int c = waitKey(30);
+        if (c == 'q' || c == 'Q' || (c & 255) == 27)
+            break;
+    }
+
+    return 0;
+}

modules/bgsegm/src/precomp.hpp

+/*
+
+By downloading, copying, installing or using the software you agree to this
+license. If you do not agree to this license, do not download, install,
+copy or use the software.
+
+
+                          License Agreement
+               For Open Source Computer Vision Library
+                       (3-clause BSD License)
+
+Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+Third party copyrights are property of their respective owners.
+
+Redistribution and use in source and binary forms, with or without modification,
+are permitted provided that the following conditions are met:
+
+  * Redistributions of source code must retain the above copyright notice,
+    this list of conditions and the following disclaimer.
+
+  * Redistributions in binary form must reproduce the above copyright notice,
+    this list of conditions and the following disclaimer in the documentation
+    and/or other materials provided with the distribution.
+
+  * Neither the names of the copyright holders nor the names of the contributors
+    may be used to endorse or promote products derived from this software
+    without specific prior written permission.
+
+This software is provided by the copyright holders and contributors "as is" and
+any express or implied warranties, including, but not limited to, the implied
+warranties of merchantability and fitness for a particular purpose are
+disclaimed. In no event shall copyright holders or contributors be liable for
+any direct, indirect, incidental, special, exemplary, or consequential damages
+(including, but not limited to, procurement of substitute goods or services;
+loss of use, data, or profits; or business interruption) however caused
+and on any theory of liability, whether in contract, strict liability,
+or tort (including negligence or otherwise) arising in any way out of
+the use of this software, even if advised of the possibility of such damage.
+
+*/
+
+#ifndef __OPENCV_BGSEGM_PRECOMP_HPP__
+#define __OPENCV_BGSEGM_PRECOMP_HPP__
+
+#include <opencv2/bgsegm.hpp>
+#include <opencv2/video.hpp>
+#include <opencv2/imgproc.hpp>
+
+#include <algorithm>
+#include <cmath>
+
+#endif

modules/bgsegm/test/test_backgroundsubtractor_gbh.cpp

+/*
+ * BackgroundSubtractorGBH_test.cpp
+ *
+ *  Created on: Jun 14, 2012
+ *      Author: andrewgodbehere
+ */
+
+#include "test_precomp.hpp"
+
+using namespace cv;
+using namespace cv::bgsegm;
+
+class CV_BackgroundSubtractorTest : public cvtest::BaseTest
+{
+public:
+    CV_BackgroundSubtractorTest();
+protected:
+    void run(int);
+};
+
+CV_BackgroundSubtractorTest::CV_BackgroundSubtractorTest()
+{
+}
+
+/**
+ * This test checks the following:
+ * (i) BackgroundSubtractorGMG can operate with matrices of various types and sizes
+ * (ii) Training mode returns empty fgmask
+ * (iii) End of training mode, and anomalous frame yields every pixel detected as FG
+ */
+void CV_BackgroundSubtractorTest::run(int)
+{
+    int code = cvtest::TS::OK;
+    RNG& rng = ts->get_rng();
+    int type = ((unsigned int)rng)%7;  //!< pick a random type, 0 - 6, defined in types_c.h
+    int channels = 1 + ((unsigned int)rng)%4;  //!< random number of channels from 1 to 4.
+    int channelsAndType = CV_MAKETYPE(type,channels);
+    int width = 2 + ((unsigned int)rng)%98; //!< Mat will be 2 to 100 in width and height
+    int height = 2 + ((unsigned int)rng)%98;
+
+    Ptr<BackgroundSubtractorGMG> fgbg = createBackgroundSubtractorGMG();
+    Mat fgmask;
+
+    if (!fgbg)
+        CV_Error(Error::StsError,"Failed to create Algorithm\n");
+
+    /**
+     * Set a few parameters
+     */
+    fgbg->setSmoothingRadius(7);
+    fgbg->setDecisionThreshold(0.7);
+    fgbg->setNumFrames(120);
+
+    /**
+     * Generate bounds for the values in the matrix for each type
+     */
+    double maxd = 0, mind = 0;
+
+    /**
+     * Max value for simulated images picked randomly in upper half of type range
+     * Min value for simulated images picked randomly in lower half of type range
+     */
+    if (type == CV_8U)
+    {
+        uchar half = UCHAR_MAX/2;
+        maxd = (unsigned char)rng.uniform(half+32, UCHAR_MAX);
+        mind = (unsigned char)rng.uniform(0, half-32);
+    }
+    else if (type == CV_8S)
+    {
+        maxd = (char)rng.uniform(32, CHAR_MAX);
+        mind = (char)rng.uniform(CHAR_MIN, -32);
+    }
+    else if (type == CV_16U)
+    {
+        ushort half = USHRT_MAX/2;
+        maxd = (unsigned int)rng.uniform(half+32, USHRT_MAX);
+        mind = (unsigned int)rng.uniform(0, half-32);
+    }
+    else if (type == CV_16S)
+    {
+        maxd = rng.uniform(32, SHRT_MAX);
+        mind = rng.uniform(SHRT_MIN, -32);
+    }
+    else if (type == CV_32S)
+    {
+        maxd = rng.uniform(32, INT_MAX);
+        mind = rng.uniform(INT_MIN, -32);
+    }
+    else if (type == CV_32F)
+    {
+        maxd = rng.uniform(32.0f, FLT_MAX);
+        mind = rng.uniform(-FLT_MAX, -32.0f);
+    }
+    else if (type == CV_64F)
+    {
+        maxd = rng.uniform(32.0, DBL_MAX);
+        mind = rng.uniform(-DBL_MAX, -32.0);
+    }
+
+    fgbg->setMinVal(mind);
+    fgbg->setMaxVal(maxd);
+
+    Mat simImage = Mat::zeros(height, width, channelsAndType);
+    int numLearningFrames = 120;
+    for (int i = 0; i < numLearningFrames; ++i)
+    {
+        /**
+         * Genrate simulated "image" for any type. Values always confined to upper half of range.
+         */
+        rng.fill(simImage, RNG::UNIFORM, (mind + maxd)*0.5, maxd);
+
+        /**
+         * Feed simulated images into background subtractor
+         */
+        fgbg->apply(simImage,fgmask);
+        Mat fullbg = Mat::zeros(simImage.rows, simImage.cols, CV_8U);
+
+        //! fgmask should be entirely background during training
+        code = cvtest::cmpEps2( ts, fgmask, fullbg, 0, false, "The training foreground mask" );
+        if (code < 0)
+            ts->set_failed_test_info( code );
+    }
+    //! generate last image, distinct from training images
+    rng.fill(simImage, RNG::UNIFORM, mind, maxd);
+
+    fgbg->apply(simImage,fgmask);
+    //! now fgmask should be entirely foreground
+    Mat fullfg = 255*Mat::ones(simImage.rows, simImage.cols, CV_8U);
+    code = cvtest::cmpEps2( ts, fgmask, fullfg, 255, false, "The final foreground mask" );
+    if (code < 0)
+    {
+        ts->set_failed_test_info( code );
+    }
+
+}
+
+TEST(VIDEO_BGSUBGMG, accuracy) { CV_BackgroundSubtractorTest test; test.safe_run(); }

modules/bgsegm/test/test_main.cpp

+#include "test_precomp.hpp"
+
+CV_TEST_MAIN("cv")

modules/bgsegm/test/test_precomp.hpp

+#ifdef __GNUC__
+#  pragma GCC diagnostic ignored "-Wmissing-declarations"
+#  if defined __clang__ || defined __APPLE__
+#    pragma GCC diagnostic ignored "-Wmissing-prototypes"
+#    pragma GCC diagnostic ignored "-Wextra"
+#  endif
+#endif
+
+#ifndef __OPENCV_TEST_PRECOMP_HPP__
+#define __OPENCV_TEST_PRECOMP_HPP__
+
+#include <iostream>
+#include "opencv2/ts.hpp"
+#include "opencv2/imgproc.hpp"
+#include "opencv2/bgsegm.hpp"
+#include "opencv2/imgcodecs.hpp"
+
+#endif

modules/bioinspired/include/opencv2/bioinspired/transientareassegmentationmodule.hpp

@@ -137,12 +137,12 @@ public:
      * @param newParameters : a parameters structures updated with the new target configuration
      * @param applyDefaultSetupOnFailure : set to true if an error must be thrown on error
      */
-    virtual void setup(TransientAreasSegmentationModule::SegmentationParameters newParameters)=0;
+    virtual void setup(SegmentationParameters newParameters)=0;
 
     /**
      * @return the current parameters setup
      */
-    virtual struct TransientAreasSegmentationModule::TransientAreasSegmentationModule::SegmentationParameters getParameters()=0;
+    virtual SegmentationParameters getParameters()=0;
 
     /**
      * parameters setup display method

modules/bioinspired/samples/retinaDemo.cpp

+//============================================================================
+// Name        : retinademo.cpp
+// Author      : Alexandre Benoit, benoit.alexandre.vision@gmail.com
+// Version     : 0.1
+// Copyright   : LISTIC/GIPSA French Labs, july 2011
+// Description : Gipsa/LISTIC Labs retina demo in C++, Ansi-style
+//============================================================================
+
+#include <iostream>
+#include <cstring>
+
+#include "opencv2/bioinspired.hpp"
+#include "opencv2/imgcodecs.hpp"
+#include "opencv2/videoio.hpp"
+#include "opencv2/highgui.hpp"
+
+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;
+}
+
+int main(int argc, char* argv[])
+{
+	// 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 retina model allows spatio-temporal image processing (applied on still images, video 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 benoit.alexandre.vision@gmail.com"<<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;
+
+	// 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];
+
+	// 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
+
+	//////////////////////////////////////////////////////////////////////////////
+	// 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;
+		}
+
+	if (inputFrame.empty())
+	{
+		help("Input media could not be loaded, aborting");
+		return -1;
+	}
+
+
+	//////////////////////////////////////////////////////////////////////////////
+	// Program start in a try/catch safety context (Retina may throw errors)
+	try
+	{
+		// create a retina instance with default parameters setup, uncomment the initialisation you wanna test
+		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::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());
+
+		// 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");
+
+		// load parameters if file exists
+		myRetina->setup("RetinaSpecificParameters.xml");
+		myRetina->clearBuffers();
+
+		// declare retina output buffers
+		cv::Mat retinaOutput_parvo;
+		cv::Mat retinaOutput_magno;
+
+		// processing loop with stop condition
+		bool continueProcessing=true; // FIXME : not yet managed during process...
+		while(continueProcessing)
+		{
+			// if using video stream, then, grabbing a new frame, else, input remains the same
+			if (videoCapture.isOpened())
+				videoCapture>>inputFrame;
+
+			// 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);
+		}
+	}catch(cv::Exception e)
+	{
+		std::cerr<<"Error using Retina : "<<e.what()<<std::endl;
+	}
+
+	// Program end message
+	std::cout<<"Retina demo end"<<std::endl;
+
+	return 0;
+}

modules/bioinspired/src/transientareassegmentationmodule.cpp

@@ -86,7 +86,8 @@ namespace cv
 namespace bioinspired
 {
 
-class TransientAreasSegmentationModuleImpl: protected BasicRetinaFilter
+class TransientAreasSegmentationModuleImpl :
+	protected BasicRetinaFilter
 {
 public:
 
@@ -136,7 +137,7 @@ public:
     /**
      * @return the current parameters setup
      */
-    struct TransientAreasSegmentationModule::TransientAreasSegmentationModule::SegmentationParameters getParameters();
+    struct TransientAreasSegmentationModule::SegmentationParameters getParameters();
 
     /**
      * parameters setup display method
@@ -219,19 +220,21 @@ protected:
     // Buffer conversion utilities
     void _convertValarrayBuffer2cvMat(const std::valarray<bool> &grayMatrixToConvert, const unsigned int nbRows, const unsigned int nbColumns, OutputArray outBuffer);
     bool _convertCvMat2ValarrayBuffer(InputArray inputMat, std::valarray<float> &outputValarrayMatrix);
+	
+	const TransientAreasSegmentationModuleImpl & operator = (const TransientAreasSegmentationModuleImpl &);
 };
 
 class TransientAreasSegmentationModuleImpl_: public  TransientAreasSegmentationModule
 {
 public:
-TransientAreasSegmentationModuleImpl_(const Size size):_segmTool(size){};
+	TransientAreasSegmentationModuleImpl_(const Size size):_segmTool(size){};
     inline virtual Size getSize(){return _segmTool.getSize();};
     inline virtual void write( cv::FileStorage& fs ) const{_segmTool.write(fs);};
     inline virtual void setup(String segmentationParameterFile, const bool applyDefaultSetupOnFailure){_segmTool.setup(segmentationParameterFile, applyDefaultSetupOnFailure);};
     inline virtual void setup(cv::FileStorage &fs, const bool applyDefaultSetupOnFailure){_segmTool.setup(fs, applyDefaultSetupOnFailure);};
     inline virtual void setup(TransientAreasSegmentationModule::SegmentationParameters newParameters){_segmTool.setup(newParameters);};
     inline virtual const String printSetup(){return _segmTool.printSetup();};
-    inline virtual struct TransientAreasSegmentationModule::TransientAreasSegmentationModule::SegmentationParameters getParameters(){return _segmTool.getParameters();};
+    inline virtual struct TransientAreasSegmentationModule::SegmentationParameters getParameters(){return _segmTool.getParameters();};
     inline virtual void write( String fs ) const{_segmTool.write(fs);};
     inline virtual void run(InputArray inputToSegment, const int channelIndex){_segmTool.run(inputToSegment, channelIndex);};
     inline virtual void getSegmentationPicture(OutputArray transientAreas){return _segmTool.getSegmentationPicture(transientAreas);};

modules/face/CMakeLists.txt

+set(the_description "Face recognition etc")
+ocv_define_module(face opencv_core opencv_imgproc)

modules/face/doc/face.rst

+***************************************
+contrib. Contributed/Experimental Stuff
+***************************************
+
+The module contains some recently added functionality that has not been stabilized, or functionality that is considered optional.
+
+.. toctree::
+    :maxdepth: 2
+
+    FaceRecognizer Documentation <facerec/index>

modules/face/doc/facerec/facerec_changelog.rst

+Changelog
+=========
+
+Release 0.05
+------------
+
+This library is now included in the official OpenCV distribution (from 2.4 on).
+The :ocv:class`FaceRecognizer` is now an :ocv:class:`Algorithm`, which better fits into the overall
+OpenCV API.
+
+To reduce the confusion on user side and minimize my work, libfacerec and OpenCV
+have been synchronized and are now based on the same interfaces and implementation.
+
+The library now has an extensive documentation:
+
+* The API is explained in detail and with a lot of code examples.
+* The face recognition guide I had written for Python and GNU Octave/MATLAB has been adapted to the new OpenCV C++ ``cv::FaceRecognizer``.
+* A tutorial for gender classification with Fisherfaces.
+* A tutorial for face recognition in videos (e.g. webcam).
+
+
+Release highlights
+++++++++++++++++++
+
+* There are no single highlights to pick from, this release is a highlight itself.
+
+Release 0.04
+------------
+
+This version is fully Windows-compatible and works with OpenCV 2.3.1. Several
+bugfixes, but none influenced the recognition rate.
+
+Release highlights
+++++++++++++++++++
+
+* A whole lot of exceptions with meaningful error messages.
+* A tutorial for Windows users: `http://bytefish.de/blog/opencv_visual_studio_and_libfacerec <http://bytefish.de/blog/opencv_visual_studio_and_libfacerec>`_
+
+
+Release 0.03
+------------
+
+Reworked the library to provide separate implementations in cpp files, because
+it's the preferred way of contributing OpenCV libraries. This means the library
+is not header-only anymore. Slight API changes were done, please see the
+documentation for details.
+
+Release highlights
+++++++++++++++++++
+
+* New Unit Tests (for LBP Histograms) make the library more robust.
+* Added more documentation.
+
+
+Release 0.02
+------------
+
+Reworked the library to provide separate implementations in cpp files, because
+it's the preferred way of contributing OpenCV libraries. This means the library
+is not header-only anymore. Slight API changes were done, please see the
+documentation for details.
+
+Release highlights
+++++++++++++++++++
+
+* New Unit Tests (for LBP Histograms) make the library more robust.
+* Added a documentation and changelog in reStructuredText.
+
+Release 0.01
+------------
+
+Initial release as header-only library.
+
+Release highlights
+++++++++++++++++++
+
+* Colormaps for OpenCV to enhance the visualization.
+* Face Recognition algorithms implemented:
+
+  * Eigenfaces [TP91]_
+  * Fisherfaces [BHK97]_
+  * Local Binary Patterns Histograms [AHP04]_
+
+* Added persistence facilities to store the models with a common API.
+* Unit Tests (using `gtest <http://code.google.com/p/googletest/>`_).
+* Providing a CMakeLists.txt to enable easy cross-platform building.

modules/face/doc/facerec/index.rst

+FaceRecognizer - Face Recognition with OpenCV
+##############################################
+
+OpenCV 2.4 now comes with the very new :ocv:class:`FaceRecognizer` class for face recognition. This documentation is going to explain you :doc:`the API <facerec_api>` in detail and it will give you a lot of help to get started (full source code examples). :doc:`Face Recognition with OpenCV <facerec_tutorial>` is the definite guide to the new :ocv:class:`FaceRecognizer`. There's also a :doc:`tutorial on gender classification <tutorial/facerec_gender_classification>`, a :doc:`tutorial for face recognition in videos <tutorial/facerec_video_recognition>` and it's shown :doc:`how to load & save your results <tutorial/facerec_save_load>`.
+
+These documents are the help I have wished for, when I was working myself into face recognition. I hope you also think the new :ocv:class:`FaceRecognizer` is a useful addition to OpenCV.
+
+Please issue any feature requests and/or bugs on the official OpenCV bug tracker at:
+
+  * http://code.opencv.org/projects/opencv/issues
+
+Contents
+========
+
+
+.. toctree::
+   :maxdepth: 1
+
+   FaceRecognizer API <facerec_api>
+   Guide to Face Recognition with OpenCV <facerec_tutorial>
+   Tutorial on Gender Classification <tutorial/facerec_gender_classification>
+   Tutorial on Face Recognition in Videos <tutorial/facerec_video_recognition>
+   Tutorial On Saving & Loading a FaceRecognizer <tutorial/facerec_save_load>
+   Changelog <facerec_changelog>
+
+Indices and tables
+==================
+
+* :ref:`genindex`
+* :ref:`modindex`
+* :ref:`search`

modules/face/doc/facerec/src/CMakeLists.txt

+CMAKE_MINIMUM_REQUIRED(VERSION 2.6)
+
+set(name "facerec")
+project(facerec_cpp_samples)
+
+#SET(OpenCV_DIR /path/to/your/opencv/installation)
+
+# packages
+find_package(OpenCV REQUIRED) # http://opencv.org
+
+# probably you should loop through the sample files here
+add_executable(facerec_demo facerec_demo.cpp)
+target_link_libraries(facerec_demo opencv_core opencv_face opencv_imgproc opencv_highgui)
+
+add_executable(facerec_video facerec_video.cpp)
+target_link_libraries(facerec_video opencv_face opencv_core opencv_imgproc opencv_highgui opencv_objdetect opencv_imgproc)
+
+add_executable(facerec_eigenfaces facerec_eigenfaces.cpp)
+target_link_libraries(facerec_eigenfaces opencv_face opencv_core opencv_imgproc opencv_highgui)
+
+add_executable(facerec_fisherfaces facerec_fisherfaces.cpp)
+target_link_libraries(facerec_fisherfaces opencv_face opencv_core opencv_imgproc opencv_highgui)
+
+add_executable(facerec_lbph facerec_lbph.cpp)
+target_link_libraries(facerec_lbph opencv_face opencv_core opencv_imgproc opencv_highgui)

modules/face/doc/facerec/src/create_csv.py

+#!/usr/bin/env python
+
+import sys
+import os.path
+
+# This is a tiny script to help you creating a CSV file from a face
+# database with a similar hierarchie:
+#
+#  philipp@mango:~/facerec/data/at$ tree
+#  .
+#  |-- README
+#  |-- s1
+#  |   |-- 1.pgm
+#  |   |-- ...
+#  |   |-- 10.pgm
+#  |-- s2
+#  |   |-- 1.pgm
+#  |   |-- ...
+#  |   |-- 10.pgm
+#  ...
+#  |-- s40
+#  |   |-- 1.pgm
+#  |   |-- ...
+#  |   |-- 10.pgm
+#
+
+if __name__ == "__main__":
+
+    if len(sys.argv) != 2:
+        print "usage: create_csv <base_path>"
+        sys.exit(1)
+
+    BASE_PATH=sys.argv[1]
+    SEPARATOR=";"
+
+    label = 0
+    for dirname, dirnames, filenames in os.walk(BASE_PATH):
+        for subdirname in dirnames:
+            subject_path = os.path.join(dirname, subdirname)
+            for filename in os.listdir(subject_path):
+                abs_path = "%s/%s" % (subject_path, filename)
+                print "%s%s%d" % (abs_path, SEPARATOR, label)
+            label = label + 1

modules/face/doc/facerec/src/crop_face.py

+#!/usr/bin/env python
+# Software License Agreement (BSD License)
+#
+# Copyright (c) 2012, Philipp Wagner
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions
+# are met:
+#
+#  * Redistributions of source code must retain the above copyright
+#    notice, this list of conditions and the following disclaimer.
+#  * Redistributions in binary form must reproduce the above
+#    copyright notice, this list of conditions and the following
+#    disclaimer in the documentation and/or other materials provided
+#    with the distribution.
+#  * Neither the name of the author nor the names of its
+#    contributors may be used to endorse or promote products derived
+#    from this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
+# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
+# COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
+# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
+# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
+# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
+# ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+# POSSIBILITY OF SUCH DAMAGE.
+
+import sys, math, Image
+
+def Distance(p1,p2):
+  dx = p2[0] - p1[0]
+  dy = p2[1] - p1[1]
+  return math.sqrt(dx*dx+dy*dy)
+
+def ScaleRotateTranslate(image, angle, center = None, new_center = None, scale = None, resample=Image.BICUBIC):
+  if (scale is None) and (center is None):
+    return image.rotate(angle=angle, resample=resample)
+  nx,ny = x,y = center
+  sx=sy=1.0
+  if new_center:
+    (nx,ny) = new_center
+  if scale:
+    (sx,sy) = (scale, scale)
+  cosine = math.cos(angle)
+  sine = math.sin(angle)
+  a = cosine/sx
+  b = sine/sx
+  c = x-nx*a-ny*b
+  d = -sine/sy
+  e = cosine/sy
+  f = y-nx*d-ny*e
+  return image.transform(image.size, Image.AFFINE, (a,b,c,d,e,f), resample=resample)
+
+def CropFace(image, eye_left=(0,0), eye_right=(0,0), offset_pct=(0.2,0.2), dest_sz = (70,70)):
+  # calculate offsets in original image
+  offset_h = math.floor(float(offset_pct[0])*dest_sz[0])
+  offset_v = math.floor(float(offset_pct[1])*dest_sz[1])
+  # get the direction
+  eye_direction = (eye_right[0] - eye_left[0], eye_right[1] - eye_left[1])
+  # calc rotation angle in radians
+  rotation = -math.atan2(float(eye_direction[1]),float(eye_direction[0]))
+  # distance between them
+  dist = Distance(eye_left, eye_right)
+  # calculate the reference eye-width
+  reference = dest_sz[0] - 2.0*offset_h
+  # scale factor
+  scale = float(dist)/float(reference)
+  # rotate original around the left eye
+  image = ScaleRotateTranslate(image, center=eye_left, angle=rotation)
+  # crop the rotated image
+  crop_xy = (eye_left[0] - scale*offset_h, eye_left[1] - scale*offset_v)
+  crop_size = (dest_sz[0]*scale, dest_sz[1]*scale)
+  image = image.crop((int(crop_xy[0]), int(crop_xy[1]), int(crop_xy[0]+crop_size[0]), int(crop_xy[1]+crop_size[1])))
+  # resize it
+  image = image.resize(dest_sz, Image.ANTIALIAS)
+  return image
+
+def readFileNames():
+    try:
+        inFile = open('path_to_created_csv_file.csv')
+    except:
+        raise IOError('There is no file named path_to_created_csv_file.csv in current directory.')
+        return False
+
+    picPath = []
+    picIndex = []
+
+    for line in inFile.readlines():
+        if line != '':
+            fields = line.rstrip().split(';')
+            picPath.append(fields[0])
+            picIndex.append(int(fields[1]))
+
+    return (picPath, picIndex)
+
+
+if __name__ == "__main__":
+  [images, indexes]=readFileNames()
+if not os.path.exists("modified"):
+    os.makedirs("modified")
+for img in images:
+    image =  Image.open(img)
+    CropFace(image, eye_left=(252,364), eye_right=(420,366), offset_pct=(0.1,0.1), dest_sz=(200,200)).save("modified/"+img.rstrip().split('/')[1]+"_10_10_200_200.jpg")
+    CropFace(image, eye_left=(252,364), eye_right=(420,366), offset_pct=(0.2,0.2), dest_sz=(200,200)).save("modified/"+img.rstrip().split('/')[1]+"_20_20_200_200.jpg")
+    CropFace(image, eye_left=(252,364), eye_right=(420,366), offset_pct=(0.3,0.3), dest_sz=(200,200)).save("modified/"+img.rstrip().split('/')[1]+"_30_30_200_200.jpg")
+    CropFace(image, eye_left=(252,364), eye_right=(420,366), offset_pct=(0.2,0.2)).save("modified/"+img.rstrip().split('/')[1]+"_20_20_70_70.jpg")

modules/face/doc/facerec/src/facerec_demo.cpp

+/*
+ * Copyright (c) 2011. Philipp Wagner <bytefish[at]gmx[dot]de>.
+ * Released to public domain under terms of the BSD Simplified license.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions are met:
+ *   * Redistributions of source code must retain the above copyright
+ *     notice, this list of conditions and the following disclaimer.
+ *   * Redistributions in binary form must reproduce the above copyright
+ *     notice, this list of conditions and the following disclaimer in the
+ *     documentation and/or other materials provided with the distribution.
+ *   * Neither the name of the organization nor the names of its contributors
+ *     may be used to endorse or promote products derived from this software
+ *     without specific prior written permission.
+ *
+ *   See <http://www.opensource.org/licenses/bsd-license>
+ */
+
+#include "opencv2/core.hpp"
+#include "opencv2/face.hpp"
+#include "opencv2/highgui.hpp"
+
+#include <iostream>
+#include <fstream>
+#include <sstream>
+
+using namespace cv;
+using namespace cv::face;
+using namespace std;
+
+static Mat norm_0_255(InputArray _src) {
+    Mat src = _src.getMat();
+    // Create and return normalized image:
+    Mat dst;
+    switch(src.channels()) {
+    case 1:
+        cv::normalize(_src, dst, 0, 255, NORM_MINMAX, CV_8UC1);
+        break;
+    case 3:
+        cv::normalize(_src, dst, 0, 255, NORM_MINMAX, CV_8UC3);
+        break;
+    default:
+        src.copyTo(dst);
+        break;
+    }
+    return dst;
+}
+
+static void read_csv(const string& filename, vector<Mat>& images, vector<int>& labels, char separator = ';') {
+    std::ifstream file(filename.c_str(), ifstream::in);
+    if (!file) {
+        string error_message = "No valid input file was given, please check the given filename.";
+        CV_Error(CV_StsBadArg, error_message);
+    }
+    string line, path, classlabel;
+    while (getline(file, line)) {
+        stringstream liness(line);
+        getline(liness, path, separator);
+        getline(liness, classlabel);
+        if(!path.empty() && !classlabel.empty()) {
+            images.push_back(imread(path, 0));
+            labels.push_back(atoi(classlabel.c_str()));
+        }
+    }
+}
+
+int main(int argc, const char *argv[]) {
+    // Check for valid command line arguments, print usage
+    // if no arguments were given.
+    if (argc != 2) {
+        cout << "usage: " << argv[0] << " <csv.ext>" << endl;
+        exit(1);
+    }
+    // Get the path to your CSV.
+    string fn_csv = string(argv[1]);
+    // These vectors hold the images and corresponding labels.
+    vector<Mat> images;
+    vector<int> labels;
+    // Read in the data. This can fail if no valid
+    // input filename is given.
+    try {
+        read_csv(fn_csv, images, labels);
+    } catch (cv::Exception& e) {
+        cerr << "Error opening file \"" << fn_csv << "\". Reason: " << e.msg << endl;
+        // nothing more we can do
+        exit(1);
+    }
+    // Quit if there are not enough images for this demo.
+    if(images.size() <= 1) {
+        string error_message = "This demo needs at least 2 images to work. Please add more images to your data set!";
+        CV_Error(CV_StsError, error_message);
+    }
+    // Get the height from the first image. We'll need this
+    // later in code to reshape the images to their original
+    // size:
+    int height = images[0].rows;
+    // The following lines simply get the last images from
+    // your dataset and remove it from the vector. This is
+    // done, so that the training data (which we learn the
+    // cv::FaceRecognizer on) and the test data we test
+    // the model with, do not overlap.
+    Mat testSample = images[images.size() - 1];
+    int testLabel = labels[labels.size() - 1];
+    images.pop_back();
+    labels.pop_back();
+    // The following lines create an Eigenfaces model for
+    // face recognition and train it with the images and
+    // labels read from the given CSV file.
+    // This here is a full PCA, if you just want to keep
+    // 10 principal components (read Eigenfaces), then call
+    // the factory method like this:
+    //
+    //      cv::createEigenFaceRecognizer(10);
+    //
+    // If you want to create a FaceRecognizer with a
+    // confidennce threshold, call it with:
+    //
+    //      cv::createEigenFaceRecognizer(10, 123.0);
+    //
+    Ptr<FaceRecognizer> model = createFisherFaceRecognizer();
+    model->train(images, labels);
+    // The following line predicts the label of a given
+    // test image:
+    int predictedLabel = model->predict(testSample);
+    //
+    // To get the confidence of a prediction call the model with:
+    //
+    //      int predictedLabel = -1;
+    //      double confidence = 0.0;
+    //      model->predict(testSample, predictedLabel, confidence);
+    //
+    string result_message = format("Predicted class = %d / Actual class = %d.", predictedLabel, testLabel);
+    cout << result_message << endl;
+    // Sometimes you'll need to get/set internal model data,
+    // which isn't exposed by the public cv::FaceRecognizer.
+    // Since each cv::FaceRecognizer is derived from a
+    // cv::Algorithm, you can query the data.
+    //
+    // First we'll use it to set the threshold of the FaceRecognizer
+    // to 0.0 without retraining the model. This can be useful if
+    // you are evaluating the model:
+    //
+    model->set("threshold", 0.0);
+    // Now the threshold of this model is set to 0.0. A prediction
+    // now returns -1, as it's impossible to have a distance below
+    // it
+    predictedLabel = model->predict(testSample);
+    cout << "Predicted class = " << predictedLabel << endl;
+    // Here is how to get the eigenvalues of this Eigenfaces model:
+    Mat eigenvalues = model->getMat("eigenvalues");
+    // And we can do the same to display the Eigenvectors (read Eigenfaces):
+    Mat W = model->getMat("eigenvectors");
+    // From this we will display the (at most) first 10 Eigenfaces:
+    for (int i = 0; i < min(10, W.cols); i++) {
+        string msg = format("Eigenvalue #%d = %.5f", i, eigenvalues.at<double>(i));
+        cout << msg << endl;
+        // get eigenvector #i
+        Mat ev = W.col(i).clone();
+        // Reshape to original size & normalize to [0...255] for imshow.
+        Mat grayscale = norm_0_255(ev.reshape(1, height));
+        // Show the image & apply a Jet colormap for better sensing.
+        Mat cgrayscale;
+        applyColorMap(grayscale, cgrayscale, COLORMAP_JET);
+        imshow(format("%d", i), cgrayscale);
+    }
+    waitKey(0);
+
+    return 0;
+}

modules/face/doc/facerec/src/facerec_eigenfaces.cpp

+/*
+ * Copyright (c) 2011. Philipp Wagner <bytefish[at]gmx[dot]de>.
+ * Released to public domain under terms of the BSD Simplified license.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions are met:
+ *   * Redistributions of source code must retain the above copyright
+ *     notice, this list of conditions and the following disclaimer.
+ *   * Redistributions in binary form must reproduce the above copyright
+ *     notice, this list of conditions and the following disclaimer in the
+ *     documentation and/or other materials provided with the distribution.
+ *   * Neither the name of the organization nor the names of its contributors
+ *     may be used to endorse or promote products derived from this software
+ *     without specific prior written permission.
+ *
+ *   See <http://www.opensource.org/licenses/bsd-license>
+ */
+
+#include "opencv2/core.hpp"
+#include "opencv2/face.hpp"
+#include "opencv2/highgui.hpp"
+
+#include <iostream>
+#include <fstream>
+#include <sstream>
+
+using namespace cv;
+using namespace cv::face;
+using namespace std;
+
+static Mat norm_0_255(InputArray _src) {
+    Mat src = _src.getMat();
+    // Create and return normalized image:
+    Mat dst;
+    switch(src.channels()) {
+    case 1:
+        cv::normalize(_src, dst, 0, 255, NORM_MINMAX, CV_8UC1);
+        break;
+    case 3:
+        cv::normalize(_src, dst, 0, 255, NORM_MINMAX, CV_8UC3);
+        break;
+    default:
+        src.copyTo(dst);
+        break;
+    }
+    return dst;
+}
+
+static void read_csv(const string& filename, vector<Mat>& images, vector<int>& labels, char separator = ';') {
+    std::ifstream file(filename.c_str(), ifstream::in);
+    if (!file) {
+        string error_message = "No valid input file was given, please check the given filename.";
+        CV_Error(CV_StsBadArg, error_message);
+    }
+    string line, path, classlabel;
+    while (getline(file, line)) {
+        stringstream liness(line);
+        getline(liness, path, separator);
+        getline(liness, classlabel);
+        if(!path.empty() && !classlabel.empty()) {
+            images.push_back(imread(path, 0));
+            labels.push_back(atoi(classlabel.c_str()));
+        }
+    }
+}
+
+int main(int argc, const char *argv[]) {
+    // Check for valid command line arguments, print usage
+    // if no arguments were given.
+    if (argc < 2) {
+        cout << "usage: " << argv[0] << " <csv.ext> <output_folder> " << endl;
+        exit(1);
+    }
+    string output_folder = ".";
+    if (argc == 3) {
+        output_folder = string(argv[2]);
+    }
+    // Get the path to your CSV.
+    string fn_csv = string(argv[1]);
+    // These vectors hold the images and corresponding labels.
+    vector<Mat> images;
+    vector<int> labels;
+    // Read in the data. This can fail if no valid
+    // input filename is given.
+    try {
+        read_csv(fn_csv, images, labels);
+    } catch (cv::Exception& e) {
+        cerr << "Error opening file \"" << fn_csv << "\". Reason: " << e.msg << endl;
+        // nothing more we can do
+        exit(1);
+    }
+    // Quit if there are not enough images for this demo.
+    if(images.size() <= 1) {
+        string error_message = "This demo needs at least 2 images to work. Please add more images to your data set!";
+        CV_Error(CV_StsError, error_message);
+    }
+    // Get the height from the first image. We'll need this
+    // later in code to reshape the images to their original
+    // size:
+    int height = images[0].rows;
+    // The following lines simply get the last images from
+    // your dataset and remove it from the vector. This is
+    // done, so that the training data (which we learn the
+    // cv::FaceRecognizer on) and the test data we test
+    // the model with, do not overlap.
+    Mat testSample = images[images.size() - 1];
+    int testLabel = labels[labels.size() - 1];
+    images.pop_back();
+    labels.pop_back();
+    // The following lines create an Eigenfaces model for
+    // face recognition and train it with the images and
+    // labels read from the given CSV file.
+    // This here is a full PCA, if you just want to keep
+    // 10 principal components (read Eigenfaces), then call
+    // the factory method like this:
+    //
+    //      cv::createEigenFaceRecognizer(10);
+    //
+    // If you want to create a FaceRecognizer with a
+    // confidence threshold (e.g. 123.0), call it with:
+    //
+    //      cv::createEigenFaceRecognizer(10, 123.0);
+    //
+    // If you want to use _all_ Eigenfaces and have a threshold,
+    // then call the method like this:
+    //
+    //      cv::createEigenFaceRecognizer(0, 123.0);
+    //
+    Ptr<FaceRecognizer> model = createEigenFaceRecognizer();
+    model->train(images, labels);
+    // The following line predicts the label of a given
+    // test image:
+    int predictedLabel = model->predict(testSample);
+    //
+    // To get the confidence of a prediction call the model with:
+    //
+    //      int predictedLabel = -1;
+    //      double confidence = 0.0;
+    //      model->predict(testSample, predictedLabel, confidence);
+    //
+    string result_message = format("Predicted class = %d / Actual class = %d.", predictedLabel, testLabel);
+    cout << result_message << endl;
+    // Here is how to get the eigenvalues of this Eigenfaces model:
+    Mat eigenvalues = model->getMat("eigenvalues");
+    // And we can do the same to display the Eigenvectors (read Eigenfaces):
+    Mat W = model->getMat("eigenvectors");
+    // Get the sample mean from the training data
+    Mat mean = model->getMat("mean");
+    // Display or save:
+    if(argc == 2) {
+        imshow("mean", norm_0_255(mean.reshape(1, images[0].rows)));
+    } else {
+        imwrite(format("%s/mean.png", output_folder.c_str()), norm_0_255(mean.reshape(1, images[0].rows)));
+    }
+    // Display or save the Eigenfaces:
+    for (int i = 0; i < min(10, W.cols); i++) {
+        string msg = format("Eigenvalue #%d = %.5f", i, eigenvalues.at<double>(i));
+        cout << msg << endl;
+        // get eigenvector #i
+        Mat ev = W.col(i).clone();
+        // Reshape to original size & normalize to [0...255] for imshow.
+        Mat grayscale = norm_0_255(ev.reshape(1, height));
+        // Show the image & apply a Jet colormap for better sensing.
+        Mat cgrayscale;
+        applyColorMap(grayscale, cgrayscale, COLORMAP_JET);
+        // Display or save:
+        if(argc == 2) {
+            imshow(format("eigenface_%d", i), cgrayscale);
+        } else {
+            imwrite(format("%s/eigenface_%d.png", output_folder.c_str(), i), norm_0_255(cgrayscale));
+        }
+    }
+
+    // Display or save the image reconstruction at some predefined steps:
+    for(int num_components = min(W.cols, 10); num_components < min(W.cols, 300); num_components+=15) {
+        // slice the eigenvectors from the model
+        Mat evs = Mat(W, Range::all(), Range(0, num_components));
+        Mat projection = subspaceProject(evs, mean, images[0].reshape(1,1));
+        Mat reconstruction = subspaceReconstruct(evs, mean, projection);
+        // Normalize the result:
+        reconstruction = norm_0_255(reconstruction.reshape(1, images[0].rows));
+        // Display or save:
+        if(argc == 2) {
+            imshow(format("eigenface_reconstruction_%d", num_components), reconstruction);
+        } else {
+            imwrite(format("%s/eigenface_reconstruction_%d.png", output_folder.c_str(), num_components), reconstruction);
+        }
+    }
+    // Display if we are not writing to an output folder:
+    if(argc == 2) {
+        waitKey(0);
+    }
+    return 0;
+}

modules/face/doc/facerec/src/facerec_fisherfaces.cpp

+/*
+ * Copyright (c) 2011. Philipp Wagner <bytefish[at]gmx[dot]de>.
+ * Released to public domain under terms of the BSD Simplified license.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions are met:
+ *   * Redistributions of source code must retain the above copyright
+ *     notice, this list of conditions and the following disclaimer.
+ *   * Redistributions in binary form must reproduce the above copyright
+ *     notice, this list of conditions and the following disclaimer in the
+ *     documentation and/or other materials provided with the distribution.
+ *   * Neither the name of the organization nor the names of its contributors
+ *     may be used to endorse or promote products derived from this software
+ *     without specific prior written permission.
+ *
+ *   See <http://www.opensource.org/licenses/bsd-license>
+ */
+
+#include "opencv2/core.hpp"
+#include "opencv2/face.hpp"
+#include "opencv2/highgui.hpp"
+
+#include <iostream>
+#include <fstream>
+#include <sstream>
+
+using namespace cv;
+using namespace cv::face;
+using namespace std;
+
+static Mat norm_0_255(InputArray _src) {
+    Mat src = _src.getMat();
+    // Create and return normalized image:
+    Mat dst;
+    switch(src.channels()) {
+    case 1:
+        cv::normalize(_src, dst, 0, 255, NORM_MINMAX, CV_8UC1);
+        break;
+    case 3:
+        cv::normalize(_src, dst, 0, 255, NORM_MINMAX, CV_8UC3);
+        break;
+    default:
+        src.copyTo(dst);
+        break;
+    }
+    return dst;
+}
+
+static void read_csv(const string& filename, vector<Mat>& images, vector<int>& labels, char separator = ';') {
+    std::ifstream file(filename.c_str(), ifstream::in);
+    if (!file) {
+        string error_message = "No valid input file was given, please check the given filename.";
+        CV_Error(CV_StsBadArg, error_message);
+    }
+    string line, path, classlabel;
+    while (getline(file, line)) {
+        stringstream liness(line);
+        getline(liness, path, separator);
+        getline(liness, classlabel);
+        if(!path.empty() && !classlabel.empty()) {
+            images.push_back(imread(path, 0));
+            labels.push_back(atoi(classlabel.c_str()));
+        }
+    }
+}
+
+int main(int argc, const char *argv[]) {
+    // Check for valid command line arguments, print usage
+    // if no arguments were given.
+    if (argc < 2) {
+        cout << "usage: " << argv[0] << " <csv.ext> <output_folder> " << endl;
+        exit(1);
+    }
+    string output_folder = ".";
+    if (argc == 3) {
+        output_folder = string(argv[2]);
+    }
+    // Get the path to your CSV.
+    string fn_csv = string(argv[1]);
+    // These vectors hold the images and corresponding labels.
+    vector<Mat> images;
+    vector<int> labels;
+    // Read in the data. This can fail if no valid
+    // input filename is given.
+    try {
+        read_csv(fn_csv, images, labels);
+    } catch (cv::Exception& e) {
+        cerr << "Error opening file \"" << fn_csv << "\". Reason: " << e.msg << endl;
+        // nothing more we can do
+        exit(1);
+    }
+    // Quit if there are not enough images for this demo.
+    if(images.size() <= 1) {
+        string error_message = "This demo needs at least 2 images to work. Please add more images to your data set!";
+        CV_Error(CV_StsError, error_message);
+    }
+    // Get the height from the first image. We'll need this
+    // later in code to reshape the images to their original
+    // size:
+    int height = images[0].rows;
+    // The following lines simply get the last images from
+    // your dataset and remove it from the vector. This is
+    // done, so that the training data (which we learn the
+    // cv::FaceRecognizer on) and the test data we test
+    // the model with, do not overlap.
+    Mat testSample = images[images.size() - 1];
+    int testLabel = labels[labels.size() - 1];
+    images.pop_back();
+    labels.pop_back();
+    // The following lines create an Fisherfaces model for
+    // face recognition and train it with the images and
+    // labels read from the given CSV file.
+    // If you just want to keep 10 Fisherfaces, then call
+    // the factory method like this:
+    //
+    //      cv::createFisherFaceRecognizer(10);
+    //
+    // However it is not useful to discard Fisherfaces! Please
+    // always try to use _all_ available Fisherfaces for
+    // classification.
+    //
+    // If you want to create a FaceRecognizer with a
+    // confidence threshold (e.g. 123.0) and use _all_
+    // Fisherfaces, then call it with:
+    //
+    //      cv::createFisherFaceRecognizer(0, 123.0);
+    //
+    Ptr<FaceRecognizer> model = createFisherFaceRecognizer();
+    model->train(images, labels);
+    // The following line predicts the label of a given
+    // test image:
+    int predictedLabel = model->predict(testSample);
+    //
+    // To get the confidence of a prediction call the model with:
+    //
+    //      int predictedLabel = -1;
+    //      double confidence = 0.0;
+    //      model->predict(testSample, predictedLabel, confidence);
+    //
+    string result_message = format("Predicted class = %d / Actual class = %d.", predictedLabel, testLabel);
+    cout << result_message << endl;
+    // Here is how to get the eigenvalues of this Eigenfaces model:
+    Mat eigenvalues = model->getMat("eigenvalues");
+    // And we can do the same to display the Eigenvectors (read Eigenfaces):
+    Mat W = model->getMat("eigenvectors");
+    // Get the sample mean from the training data
+    Mat mean = model->getMat("mean");
+    // Display or save:
+    if(argc == 2) {
+        imshow("mean", norm_0_255(mean.reshape(1, images[0].rows)));
+    } else {
+        imwrite(format("%s/mean.png", output_folder.c_str()), norm_0_255(mean.reshape(1, images[0].rows)));
+    }
+    // Display or save the first, at most 16 Fisherfaces:
+    for (int i = 0; i < min(16, W.cols); i++) {
+        string msg = format("Eigenvalue #%d = %.5f", i, eigenvalues.at<double>(i));
+        cout << msg << endl;
+        // get eigenvector #i
+        Mat ev = W.col(i).clone();
+        // Reshape to original size & normalize to [0...255] for imshow.
+        Mat grayscale = norm_0_255(ev.reshape(1, height));
+        // Show the image & apply a Bone colormap for better sensing.
+        Mat cgrayscale;
+        applyColorMap(grayscale, cgrayscale, COLORMAP_BONE);
+        // Display or save:
+        if(argc == 2) {
+            imshow(format("fisherface_%d", i), cgrayscale);
+        } else {
+            imwrite(format("%s/fisherface_%d.png", output_folder.c_str(), i), norm_0_255(cgrayscale));
+        }
+    }
+    // Display or save the image reconstruction at some predefined steps:
+    for(int num_component = 0; num_component < min(16, W.cols); num_component++) {
+        // Slice the Fisherface from the model:
+        Mat ev = W.col(num_component);
+        Mat projection = subspaceProject(ev, mean, images[0].reshape(1,1));
+        Mat reconstruction = subspaceReconstruct(ev, mean, projection);
+        // Normalize the result:
+        reconstruction = norm_0_255(reconstruction.reshape(1, images[0].rows));
+        // Display or save:
+        if(argc == 2) {
+            imshow(format("fisherface_reconstruction_%d", num_component), reconstruction);
+        } else {
+            imwrite(format("%s/fisherface_reconstruction_%d.png", output_folder.c_str(), num_component), reconstruction);
+        }
+    }
+    // Display if we are not writing to an output folder:
+    if(argc == 2) {
+        waitKey(0);
+    }
+    return 0;
+}

modules/face/doc/facerec/src/facerec_lbph.cpp

+/*
+ * Copyright (c) 2011. Philipp Wagner <bytefish[at]gmx[dot]de>.
+ * Released to public domain under terms of the BSD Simplified license.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions are met:
+ *   * Redistributions of source code must retain the above copyright
+ *     notice, this list of conditions and the following disclaimer.
+ *   * Redistributions in binary form must reproduce the above copyright
+ *     notice, this list of conditions and the following disclaimer in the
+ *     documentation and/or other materials provided with the distribution.
+ *   * Neither the name of the organization nor the names of its contributors
+ *     may be used to endorse or promote products derived from this software
+ *     without specific prior written permission.
+ *
+ *   See <http://www.opensource.org/licenses/bsd-license>
+ */
+
+#include "opencv2/core.hpp"
+#include "opencv2/face.hpp"
+#include "opencv2/highgui.hpp"
+
+#include <iostream>
+#include <fstream>
+#include <sstream>
+
+using namespace cv;
+using namespace cv::face;
+using namespace std;
+
+static void read_csv(const string& filename, vector<Mat>& images, vector<int>& labels, char separator = ';') {
+    std::ifstream file(filename.c_str(), ifstream::in);
+    if (!file) {
+        string error_message = "No valid input file was given, please check the given filename.";
+        CV_Error(CV_StsBadArg, error_message);
+    }
+    string line, path, classlabel;
+    while (getline(file, line)) {
+        stringstream liness(line);
+        getline(liness, path, separator);
+        getline(liness, classlabel);
+        if(!path.empty() && !classlabel.empty()) {
+            images.push_back(imread(path, 0));
+            labels.push_back(atoi(classlabel.c_str()));
+        }
+    }
+}
+
+int main(int argc, const char *argv[]) {
+    // Check for valid command line arguments, print usage
+    // if no arguments were given.
+    if (argc != 2) {
+        cout << "usage: " << argv[0] << " <csv.ext>" << endl;
+        exit(1);
+    }
+    // Get the path to your CSV.
+    string fn_csv = string(argv[1]);
+    // These vectors hold the images and corresponding labels.
+    vector<Mat> images;
+    vector<int> labels;
+    // Read in the data. This can fail if no valid
+    // input filename is given.
+    try {
+        read_csv(fn_csv, images, labels);
+    } catch (cv::Exception& e) {
+        cerr << "Error opening file \"" << fn_csv << "\". Reason: " << e.msg << endl;
+        // nothing more we can do
+        exit(1);
+    }
+    // Quit if there are not enough images for this demo.
+    if(images.size() <= 1) {
+        string error_message = "This demo needs at least 2 images to work. Please add more images to your data set!";
+        CV_Error(CV_StsError, error_message);
+    }
+    // Get the height from the first image. We'll need this
+    // later in code to reshape the images to their original
+    // size:
+    int height = images[0].rows;
+    // The following lines simply get the last images from
+    // your dataset and remove it from the vector. This is
+    // done, so that the training data (which we learn the
+    // cv::FaceRecognizer on) and the test data we test
+    // the model with, do not overlap.
+    Mat testSample = images[images.size() - 1];
+    int testLabel = labels[labels.size() - 1];
+    images.pop_back();
+    labels.pop_back();
+    // The following lines create an LBPH model for
+    // face recognition and train it with the images and
+    // labels read from the given CSV file.
+    //
+    // The LBPHFaceRecognizer uses Extended Local Binary Patterns
+    // (it's probably configurable with other operators at a later
+    // point), and has the following default values
+    //
+    //      radius = 1
+    //      neighbors = 8
+    //      grid_x = 8
+    //      grid_y = 8
+    //
+    // So if you want a LBPH FaceRecognizer using a radius of
+    // 2 and 16 neighbors, call the factory method with:
+    //
+    //      cv::createLBPHFaceRecognizer(2, 16);
+    //
+    // And if you want a threshold (e.g. 123.0) call it with its default values:
+    //
+    //      cv::createLBPHFaceRecognizer(1,8,8,8,123.0)
+    //
+    Ptr<FaceRecognizer> model = createLBPHFaceRecognizer();
+    model->train(images, labels);
+    // The following line predicts the label of a given
+    // test image:
+    int predictedLabel = model->predict(testSample);
+    //
+    // To get the confidence of a prediction call the model with:
+    //
+    //      int predictedLabel = -1;
+    //      double confidence = 0.0;
+    //      model->predict(testSample, predictedLabel, confidence);
+    //
+    string result_message = format("Predicted class = %d / Actual class = %d.", predictedLabel, testLabel);
+    cout << result_message << endl;
+    // Sometimes you'll need to get/set internal model data,
+    // which isn't exposed by the public cv::FaceRecognizer.
+    // Since each cv::FaceRecognizer is derived from a
+    // cv::Algorithm, you can query the data.
+    //
+    // First we'll use it to set the threshold of the FaceRecognizer
+    // to 0.0 without retraining the model. This can be useful if
+    // you are evaluating the model:
+    //
+    model->set("threshold", 0.0);
+    // Now the threshold of this model is set to 0.0. A prediction
+    // now returns -1, as it's impossible to have a distance below
+    // it
+    predictedLabel = model->predict(testSample);
+    cout << "Predicted class = " << predictedLabel << endl;
+    // Show some informations about the model, as there's no cool
+    // Model data to display as in Eigenfaces/Fisherfaces.
+    // Due to efficiency reasons the LBP images are not stored
+    // within the model:
+    cout << "Model Information:" << endl;
+    string model_info = format("\tLBPH(radius=%i, neighbors=%i, grid_x=%i, grid_y=%i, threshold=%.2f)",
+            model->getInt("radius"),
+            model->getInt("neighbors"),
+            model->getInt("grid_x"),
+            model->getInt("grid_y"),
+            model->getDouble("threshold"));
+    cout << model_info << endl;
+    // We could get the histograms for example:
+    vector<Mat> histograms = model->getMatVector("histograms");
+    // But should I really visualize it? Probably the length is interesting:
+    cout << "Size of the histograms: " << histograms[0].total() << endl;
+    return 0;
+}

modules/face/doc/facerec/src/facerec_save_load.cpp

+/*
+ * Copyright (c) 2011. Philipp Wagner <bytefish[at]gmx[dot]de>.
+ * Released to public domain under terms of the BSD Simplified license.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions are met:
+ *   * Redistributions of source code must retain the above copyright
+ *     notice, this list of conditions and the following disclaimer.
+ *   * Redistributions in binary form must reproduce the above copyright
+ *     notice, this list of conditions and the following disclaimer in the
+ *     documentation and/or other materials provided with the distribution.
+ *   * Neither the name of the organization nor the names of its contributors
+ *     may be used to endorse or promote products derived from this software
+ *     without specific prior written permission.
+ *
+ *   See <http://www.opensource.org/licenses/bsd-license>
+ */
+
+#include "opencv2/core.hpp"
+#include "opencv2/face.hpp"
+#include "opencv2/highgui.hpp"
+
+#include <iostream>
+#include <fstream>
+#include <sstream>
+
+using namespace cv;
+using namespace cv::face;
+using namespace std;
+
+static Mat norm_0_255(InputArray _src) {
+    Mat src = _src.getMat();
+    // Create and return normalized image:
+    Mat dst;
+    switch(src.channels()) {
+    case 1:
+        cv::normalize(_src, dst, 0, 255, NORM_MINMAX, CV_8UC1);
+        break;
+    case 3:
+        cv::normalize(_src, dst, 0, 255, NORM_MINMAX, CV_8UC3);
+        break;
+    default:
+        src.copyTo(dst);
+        break;
+    }
+    return dst;
+}
+
+static void read_csv(const string& filename, vector<Mat>& images, vector<int>& labels, char separator = ';') {
+    std::ifstream file(filename.c_str(), ifstream::in);
+    if (!file) {
+        string error_message = "No valid input file was given, please check the given filename.";
+        CV_Error(CV_StsBadArg, error_message);
+    }
+    string line, path, classlabel;
+    while (getline(file, line)) {
+        stringstream liness(line);
+        getline(liness, path, separator);
+        getline(liness, classlabel);
+        if(!path.empty() && !classlabel.empty()) {
+            images.push_back(imread(path, 0));
+            labels.push_back(atoi(classlabel.c_str()));
+        }
+    }
+}
+
+int main(int argc, const char *argv[]) {
+    // Check for valid command line arguments, print usage
+    // if no arguments were given.
+    if (argc < 2) {
+        cout << "usage: " << argv[0] << " <csv.ext> <output_folder> " << endl;
+        exit(1);
+    }
+    string output_folder = ".";
+    if (argc == 3) {
+        output_folder = string(argv[2]);
+    }
+    // Get the path to your CSV.
+    string fn_csv = string(argv[1]);
+    // These vectors hold the images and corresponding labels.
+    vector<Mat> images;
+    vector<int> labels;
+    // Read in the data. This can fail if no valid
+    // input filename is given.
+    try {
+        read_csv(fn_csv, images, labels);
+    } catch (cv::Exception& e) {
+        cerr << "Error opening file \"" << fn_csv << "\". Reason: " << e.msg << endl;
+        // nothing more we can do
+        exit(1);
+    }
+    // Quit if there are not enough images for this demo.
+    if(images.size() <= 1) {
+        string error_message = "This demo needs at least 2 images to work. Please add more images to your data set!";
+        CV_Error(CV_StsError, error_message);
+    }
+    // Get the height from the first image. We'll need this
+    // later in code to reshape the images to their original
+    // size:
+    int height = images[0].rows;
+    // The following lines simply get the last images from
+    // your dataset and remove it from the vector. This is
+    // done, so that the training data (which we learn the
+    // cv::FaceRecognizer on) and the test data we test
+    // the model with, do not overlap.
+    Mat testSample = images[images.size() - 1];
+    int testLabel = labels[labels.size() - 1];
+    images.pop_back();
+    labels.pop_back();
+    // The following lines create an Eigenfaces model for
+    // face recognition and train it with the images and
+    // labels read from the given CSV file.
+    // This here is a full PCA, if you just want to keep
+    // 10 principal components (read Eigenfaces), then call
+    // the factory method like this:
+    //
+    //      cv::createEigenFaceRecognizer(10);
+    //
+    // If you want to create a FaceRecognizer with a
+    // confidence threshold (e.g. 123.0), call it with:
+    //
+    //      cv::createEigenFaceRecognizer(10, 123.0);
+    //
+    // If you want to use _all_ Eigenfaces and have a threshold,
+    // then call the method like this:
+    //
+    //      cv::createEigenFaceRecognizer(0, 123.0);
+    //
+    Ptr<FaceRecognizer> model0 = createEigenFaceRecognizer();
+    model0->train(images, labels);
+    // save the model to eigenfaces_at.yaml
+    model0->save("eigenfaces_at.yml");
+    //
+    //
+    // Now create a new Eigenfaces Recognizer
+    //
+    Ptr<FaceRecognizer> model1 = createEigenFaceRecognizer();
+    model1->load("eigenfaces_at.yml");
+    // The following line predicts the label of a given
+    // test image:
+    int predictedLabel = model1->predict(testSample);
+    //
+    // To get the confidence of a prediction call the model with:
+    //
+    //      int predictedLabel = -1;
+    //      double confidence = 0.0;
+    //      model->predict(testSample, predictedLabel, confidence);
+    //
+    string result_message = format("Predicted class = %d / Actual class = %d.", predictedLabel, testLabel);
+    cout << result_message << endl;
+    // Here is how to get the eigenvalues of this Eigenfaces model:
+    Mat eigenvalues = model1->getMat("eigenvalues");
+    // And we can do the same to display the Eigenvectors (read Eigenfaces):
+    Mat W = model1->getMat("eigenvectors");
+    // Get the sample mean from the training data
+    Mat mean = model1->getMat("mean");
+    // Display or save:
+    if(argc == 2) {
+        imshow("mean", norm_0_255(mean.reshape(1, images[0].rows)));
+    } else {
+        imwrite(format("%s/mean.png", output_folder.c_str()), norm_0_255(mean.reshape(1, images[0].rows)));
+    }
+    // Display or save the Eigenfaces:
+    for (int i = 0; i < min(10, W.cols); i++) {
+        string msg = format("Eigenvalue #%d = %.5f", i, eigenvalues.at<double>(i));
+        cout << msg << endl;
+        // get eigenvector #i
+        Mat ev = W.col(i).clone();
+        // Reshape to original size & normalize to [0...255] for imshow.
+        Mat grayscale = norm_0_255(ev.reshape(1, height));
+        // Show the image & apply a Jet colormap for better sensing.
+        Mat cgrayscale;
+        applyColorMap(grayscale, cgrayscale, COLORMAP_JET);
+        // Display or save:
+        if(argc == 2) {
+            imshow(format("eigenface_%d", i), cgrayscale);
+        } else {
+            imwrite(format("%s/eigenface_%d.png", output_folder.c_str(), i), norm_0_255(cgrayscale));
+        }
+    }
+    // Display or save the image reconstruction at some predefined steps:
+    for(int num_components = 10; num_components < 300; num_components+=15) {
+        // slice the eigenvectors from the model
+        Mat evs = Mat(W, Range::all(), Range(0, num_components));
+        Mat projection = subspaceProject(evs, mean, images[0].reshape(1,1));
+        Mat reconstruction = subspaceReconstruct(evs, mean, projection);
+        // Normalize the result:
+        reconstruction = norm_0_255(reconstruction.reshape(1, images[0].rows));
+        // Display or save:
+        if(argc == 2) {
+            imshow(format("eigenface_reconstruction_%d", num_components), reconstruction);
+        } else {
+            imwrite(format("%s/eigenface_reconstruction_%d.png", output_folder.c_str(), num_components), reconstruction);
+        }
+    }
+    // Display if we are not writing to an output folder:
+    if(argc == 2) {
+        waitKey(0);
+    }
+    return 0;
+}

modules/face/doc/facerec/tutorial/facerec_save_load.rst

+Saving and Loading a FaceRecognizer
+===================================
+
+Introduction
+------------
+
+Saving and loading a :ocv:class:`FaceRecognizer` is very important. Training a FaceRecognizer can be a very time-intense task, plus it's often impossible to ship the whole face database to the user of your product. The task of saving and loading a FaceRecognizer is easy with :ocv:class:`FaceRecognizer`. You only have to call :ocv:func:`FaceRecognizer::load` for loading and :ocv:func:`FaceRecognizer::save` for saving a :ocv:class:`FaceRecognizer`.
+
+I'll adapt the Eigenfaces example from the :doc:`../facerec_tutorial`: Imagine we want to learn the Eigenfaces of the `AT&T Facedatabase <http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html>`_, store the model to a YAML file and then load it again.
+
+From the loaded model, we'll get a prediction, show the mean, Eigenfaces and the image reconstruction.
+
+Using FaceRecognizer::save and FaceRecognizer::load
+-----------------------------------------------------
+
+The source code for this demo application is also available in the ``src`` folder coming with this documentation:
+
+* :download:`src/facerec_save_load.cpp <../src/facerec_save_load.cpp>`
+
+.. literalinclude:: ../src/facerec_save_load.cpp
+   :language: cpp
+   :linenos:
+
+Results
+-------
+
+``eigenfaces_at.yml`` then contains the model state, we'll simply look at the first 10 lines with ``head eigenfaces_at.yml``:
+
+.. code-block:: none
+
+  philipp@mango:~/github/libfacerec-build$ head eigenfaces_at.yml
+  %YAML:1.0
+  num_components: 399
+  mean: !!opencv-matrix
+     rows: 1
+     cols: 10304
+     dt: d
+     data: [ 8.5558897243107765e+01, 8.5511278195488714e+01,
+         8.5854636591478695e+01, 8.5796992481203006e+01,
+         8.5952380952380949e+01, 8.6162907268170414e+01,
+         8.6082706766917283e+01, 8.5776942355889716e+01,
+
+And here is the Reconstruction, which is the same as the original:
+
+.. image:: ../img/eigenface_reconstruction_opencv.png
+    :align: center