Merged the trunk r8852:8880

cmake/OpenCVDetectCXXCompiler.cmake

@@ -89,7 +89,7 @@ if(CMAKE_COMPILER_IS_GNUCXX)
     endif()
 endif()
 
-if(CMAKE_SYSTEM_PROCESSOR MATCHES amd64.*|x86_64.*)
+if(CMAKE_SYSTEM_PROCESSOR MATCHES amd64.*|x86_64.* OR CMAKE_GENERATOR MATCHES "Visual Studio.*Win64")
     set(X86_64 1)
 elseif(CMAKE_SYSTEM_PROCESSOR MATCHES i686.*|i386.*|x86.*)
     set(X86 1)

modules/calib3d/src/calibration.cpp

@@ -2608,7 +2608,7 @@ void cvGetOptimalNewCameraMatrix( const CvMat* cameraMatrix, const CvMat* distCo
 
         if( validPixROI )
         {
-            icvGetRectangles( cameraMatrix, distCoeffs, 0, newCameraMatrix, imgSize, inner, outer );
+            icvGetRectangles( cameraMatrix, distCoeffs, 0, &matM, imgSize, inner, outer );
             cv::Rect r = inner;
             r &= cv::Rect(0, 0, newImgSize.width, newImgSize.height);
             *validPixROI = r;

modules/calib3d/src/fundam.cpp

@@ -1128,6 +1128,8 @@ void cv::computeCorrespondEpilines( InputArray _points, int whichImage,
 {
     Mat points = _points.getMat(), F = _Fmat.getMat();
     int npoints = points.checkVector(2);
+    if( npoints < 0 )
+        npoints = points.checkVector(3);
     CV_Assert( npoints >= 0 && (points.depth() == CV_32F || points.depth() == CV_32S));
     
     _lines.create(npoints, 1, CV_32FC3, -1, true);

modules/core/include/opencv2/core/core.hpp

@@ -1249,6 +1249,7 @@ public:
     ~Ptr();
     //! copy constructor. Copies the members and calls addref()
     Ptr(const Ptr& ptr);
+    template<typename _Tp2> Ptr(const Ptr<_Tp2>& ptr);
     //! copy operator. Calls ptr.addref() and release() before copying the members
     Ptr& operator = (const Ptr& ptr);
     //! increments the reference counter

modules/core/include/opencv2/core/eigen.hpp

@@ -51,6 +51,7 @@
 #if defined _MSC_VER && _MSC_VER >= 1200
 #pragma warning( disable: 4714 ) //__forceinline is not inlined
 #pragma warning( disable: 4127 ) //conditional expression is constant
+#pragma warning( disable: 4244 ) //conversion from '__int64' to 'int', possible loss of data
 #endif
 
 namespace cv

modules/core/include/opencv2/core/operations.hpp

@@ -2642,14 +2642,35 @@ template<typename _Tp> inline Ptr<_Tp>::operator const _Tp*() const { return obj
 
 template<typename _Tp> inline bool Ptr<_Tp>::empty() const { return obj == 0; }
 
+template<typename _Tp> template<typename _Tp2> Ptr<_Tp>::Ptr(const Ptr<_Tp2>& p)
+    : obj(0), refcount(0)
+{
+    if (p.empty())
+        return;
+    
+    _Tp* p_casted = dynamic_cast<_Tp*>(p.obj);
+    if (!p_casted)
+        return;
+
+    obj = p_casted;
+    refcount = p.refcount;
+    addref();
+}
+
 template<typename _Tp> template<typename _Tp2> inline Ptr<_Tp2> Ptr<_Tp>::ptr()
 {
     Ptr<_Tp2> p;
     if( !obj )
         return p;
+
+    _Tp2* obj_casted = dynamic_cast<_Tp2*>(obj);
+    if (!obj_casted)
+        return p;
+
     if( refcount )
         CV_XADD(refcount, 1);
-    p.obj = dynamic_cast<_Tp2*>(obj);
+
+    p.obj = obj_casted;
     p.refcount = refcount;
     return p;
 }
@@ -2659,9 +2680,15 @@ template<typename _Tp> template<typename _Tp2> inline const Ptr<_Tp2> Ptr<_Tp>::
     Ptr<_Tp2> p;
     if( !obj )
         return p;
+
+    _Tp2* obj_casted = dynamic_cast<_Tp2*>(obj);
+    if (!obj_casted)
+        return p;
+
     if( refcount )
         CV_XADD(refcount, 1);
-    p.obj = dynamic_cast<_Tp2*>(obj);
+
+    p.obj = obj_casted;
     p.refcount = refcount;
     return p;
 }

modules/core/src/algorithm.cpp

@@ -321,7 +321,8 @@ AlgorithmInfo::AlgorithmInfo(const string& _name, Algorithm::Constructor create)
 {
     data = new AlgorithmInfoData;
     data->_name = _name;
-    alglist().add(_name, create);
+    if (!alglist().find(_name, create))
+        alglist().add(_name, create);
 }
 
 AlgorithmInfo::~AlgorithmInfo()

modules/core/test/test_operations.cpp

@@ -917,6 +917,25 @@ bool CV_OperationsTest::operations1()
             if (!v10dzero[ii] == 0.0)
                 throw test_excep();
         }
+        
+        Mat A(1, 32, CV_32F), B;
+        for( int i = 0; i < A.cols; i++ )
+            A.at<float>(i) = (float)(i <= 12 ? i : 24 - i);
+        transpose(A, B);
+        
+        int minidx[2] = {0, 0}, maxidx[2] = {0, 0};
+        double minval = 0, maxval = 0;
+        minMaxIdx(A, &minval, &maxval, minidx, maxidx);
+        
+        if( !(minidx[0] == 0 && minidx[1] == 31 && maxidx[0] == 0 && maxidx[1] == 12 &&
+                  minval == -7 && maxval == 12))
+            throw test_excep();
+        
+        minMaxIdx(B, &minval, &maxval, minidx, maxidx);
+        
+        if( !(minidx[0] == 31 && minidx[1] == 0 && maxidx[0] == 12 && maxidx[1] == 0 &&
+              minval == -7 && maxval == 12))
+            throw test_excep();
     }
     catch(const test_excep&)
     {
@@ -946,13 +965,13 @@ bool CV_OperationsTest::TestSVD()
         Mat Q(3,3,CV_32FC1);
         Mat U,Vt,R,T,W;
         
-        Dp.at<float>(0,0)=0.86483884; Dp.at<float>(0,1)= -0.3077251; Dp.at<float>(0,2)=-0.55711365;
-        Dp.at<float>(1,0)=0.49294353; Dp.at<float>(1,1)=-0.24209651; Dp.at<float>(1,2)=-0.25084701;
-        Dp.at<float>(2,0)=0;          Dp.at<float>(2,1)=0;           Dp.at<float>(2,2)=0;
+        Dp.at<float>(0,0)=0.86483884f; Dp.at<float>(0,1)= -0.3077251f; Dp.at<float>(0,2)=-0.55711365f;
+        Dp.at<float>(1,0)=0.49294353f; Dp.at<float>(1,1)=-0.24209651f; Dp.at<float>(1,2)=-0.25084701f;
+        Dp.at<float>(2,0)=0;           Dp.at<float>(2,1)=0;            Dp.at<float>(2,2)=0;
         
-        Dc.at<float>(0,0)=0.75632739; Dc.at<float>(0,1)= -0.38859656; Dc.at<float>(0,2)=-0.36773083;
-        Dc.at<float>(1,0)=0.9699229; Dc.at<float>(1,1)=-0.49858192; Dc.at<float>(1,2)=-0.47134098;
-        Dc.at<float>(2,0)=0.10566688; Dc.at<float>(2,1)=-0.060333252; Dc.at<float>(2,2)=-0.045333147;
+        Dc.at<float>(0,0)=0.75632739f; Dc.at<float>(0,1)= -0.38859656f; Dc.at<float>(0,2)=-0.36773083f;
+        Dc.at<float>(1,0)=0.9699229f;  Dc.at<float>(1,1)=-0.49858192f;  Dc.at<float>(1,2)=-0.47134098f;
+        Dc.at<float>(2,0)=0.10566688f; Dc.at<float>(2,1)=-0.060333252f; Dc.at<float>(2,2)=-0.045333147f;
         
         Q=Dp*Dc.t();
         SVD decomp;

modules/features2d/src/descriptors.cpp

@@ -134,49 +134,43 @@ static void convertBGRImageToOpponentColorSpace( const Mat& bgrImage, vector<Mat
 
     // Calculate the channels of the opponent color space
     {
-        // (R - G) / sqrt(2)
+        // (R - G)/sqrt(2), but converted to the destination data type
         MatConstIterator_<signed char> rIt = bgrChannels[2].begin<signed char>();
         MatConstIterator_<signed char> gIt = bgrChannels[1].begin<signed char>();
         MatIterator_<unsigned char> dstIt = opponentChannels[0].begin<unsigned char>();
-        float factor = 1.f / sqrt(2.f);
         for( ; dstIt != opponentChannels[0].end<unsigned char>(); ++rIt, ++gIt, ++dstIt )
         {
-            int value = static_cast<int>( static_cast<float>(static_cast<int>(*gIt)-static_cast<int>(*rIt)) * factor );
-            if( value < 0 ) value = 0;
-            if( value > 255 ) value = 255;
-            (*dstIt) = static_cast<unsigned char>(value);
+            float value = 0.5f * (static_cast<int>(*gIt) -
+            					  static_cast<int>(*rIt) + 255);
+            (*dstIt) = static_cast<unsigned char>(value + 0.5f);
         }
     }
     {
-        // (R + G - 2B)/sqrt(6)
+        // (R + G - 2B)/sqrt(6), but converted to the destination data type
         MatConstIterator_<signed char> rIt = bgrChannels[2].begin<signed char>();
         MatConstIterator_<signed char> gIt = bgrChannels[1].begin<signed char>();
         MatConstIterator_<signed char> bIt = bgrChannels[0].begin<signed char>();
         MatIterator_<unsigned char> dstIt = opponentChannels[1].begin<unsigned char>();
-        float factor = 1.f / sqrt(6.f);
         for( ; dstIt != opponentChannels[1].end<unsigned char>(); ++rIt, ++gIt, ++bIt, ++dstIt )
         {
-            int value = static_cast<int>( static_cast<float>(static_cast<int>(*rIt) + static_cast<int>(*gIt) - 2*static_cast<int>(*bIt)) *
-                                          factor );
-            if( value < 0 ) value = 0;
-            if( value > 255 ) value = 255;
-            (*dstIt) = static_cast<unsigned char>(value);
+            float value = 0.25f * (static_cast<int>(*rIt) + static_cast<int>(*gIt) -
+                                   2*static_cast<int>(*bIt) + 510);
+            (*dstIt) = static_cast<unsigned char>(value + 0.5f);
         }
     }
     {
-        // (R + G + B)/sqrt(3)
+        // (R + G + B)/sqrt(3), but converted to the destination data type
         MatConstIterator_<signed char> rIt = bgrChannels[2].begin<signed char>();
         MatConstIterator_<signed char> gIt = bgrChannels[1].begin<signed char>();
         MatConstIterator_<signed char> bIt = bgrChannels[0].begin<signed char>();
         MatIterator_<unsigned char> dstIt = opponentChannels[2].begin<unsigned char>();
-        float factor = 1.f / sqrt(3.f);
+        float factor = 1.f/3.f;
         for( ; dstIt != opponentChannels[2].end<unsigned char>(); ++rIt, ++gIt, ++bIt, ++dstIt )
         {
-            int value = static_cast<int>( static_cast<float>(static_cast<int>(*rIt) + static_cast<int>(*gIt) + static_cast<int>(*bIt)) *
-                                          factor );
-            if( value < 0 ) value = 0;
-            if( value > 255 ) value = 255;
-            (*dstIt) = static_cast<unsigned char>(value);
+            float value = factor * (static_cast<int>(*rIt) + 
+									static_cast<int>(*gIt) + 
+									static_cast<int>(*bIt));
+            (*dstIt) = static_cast<unsigned char>(value + 0.5f);
         }
     }
 }

modules/features2d/src/freak.cpp

@@ -274,14 +274,14 @@ void FREAK::computeImpl( const Mat& image, std::vector<KeyPoint>& keypoints, Mat
     // allocate descriptor memory, estimate orientations, extract descriptors
     if( !extAll ) {
         // extract the best comparisons only
-        descriptors = cv::Mat::zeros(keypoints.size(), FREAK_NB_PAIRS/8, CV_8U);
+        descriptors = cv::Mat::zeros((int)keypoints.size(), FREAK_NB_PAIRS/8, CV_8U);
 #if CV_SSE2
         __m128i* ptr= (__m128i*) (descriptors.data+(keypoints.size()-1)*descriptors.step[0]);
         // binary: 10000000 => char: 128 or hex: 0x80
-        const __m128i binMask = _mm_set_epi8(0x80, 0x80, 0x80, 0x80,
-                                             0x80, 0x80, 0x80, 0x80,
-                                             0x80, 0x80, 0x80, 0x80,
-                                             0x80, 0x80, 0x80, 0x80);
+        const __m128i binMask = _mm_set_epi8('\x80', '\x80', '\x80', '\x80',
+                                             '\x80', '\x80', '\x80', '\x80',
+                                             '\x80', '\x80', '\x80', '\x80',
+                                             '\x80', '\x80', '\x80', '\x80');
 #else
         std::bitset<FREAK_NB_PAIRS>* ptr = (std::bitset<FREAK_NB_PAIRS>*) (descriptors.data+(keypoints.size()-1)*descriptors.step[0]);
 #endif
@@ -390,7 +390,7 @@ void FREAK::computeImpl( const Mat& image, std::vector<KeyPoint>& keypoints, Mat
         }
     }
     else { // extract all possible comparisons for selection
-        descriptors = cv::Mat::zeros(keypoints.size(), 128, CV_8U);
+        descriptors = cv::Mat::zeros((int)keypoints.size(), 128, CV_8U);
         std::bitset<1024>* ptr = (std::bitset<1024>*) (descriptors.data+(keypoints.size()-1)*descriptors.step[0]);
 
         for( size_t k = keypoints.size(); k--; ) {
@@ -522,16 +522,16 @@ vector<int> FREAK::selectPairs(const std::vector<Mat>& images
     Mat descriptorsFloat = Mat::zeros(descriptors.rows, 903, CV_32F);
 
     std::bitset<1024>* ptr = (std::bitset<1024>*) (descriptors.data+(descriptors.rows-1)*descriptors.step[0]);
-    for( size_t m = descriptors.rows; m--; ) {
-        for( size_t n = 903; n--; ) {
+    for( int m = descriptors.rows; m--; ) {
+        for( int n = 903; n--; ) {
             if( ptr->test(n) == true )
-                descriptorsFloat.at<float>(m,n)=1.0;
+                descriptorsFloat.at<float>(m,n)=1.0f;
         }
         --ptr;
     }
 
     std::vector<PairStat> pairStat;
-    for( size_t n = 903; n--; ) {
+    for( int n = 903; n--; ) {
         // the higher the variance, the better --> mean = 0.5
         PairStat tmp = { fabs( mean(descriptorsFloat.col(n))[0]-0.5 ) ,n};
         pairStat.push_back(tmp);

modules/features2d/test/test_features2d.cpp

@@ -997,7 +997,7 @@ TEST( Features2d_Detector_Harris, regression )
     test.safe_run();
 }
 
-TEST( Features2d_Detector_MSER, regression )
+TEST( Features2d_Detector_MSER, DISABLED_regression )
 {
     CV_FeatureDetectorTest test( "detector-mser", FeatureDetector::create("MSER") );
     test.safe_run();

modules/features2d/test/test_mser.cpp

@@ -203,5 +203,5 @@ void CV_MserTest::run(int)
     }
 }
 
-TEST(Features2d_MSER, regression) { CV_MserTest test; test.safe_run(); }
+TEST(Features2d_MSER, DISABLED_regression) { CV_MserTest test; test.safe_run(); }
 

modules/highgui/src/cap_dshow.cpp

@@ -3278,7 +3278,7 @@ bool CvCaptureCAM_DShow::setProperty( int property_id, double value )
 
     case CV_CAP_PROP_FPS:
         int fps = cvRound(value);
-        if (fps != VI.getFPS(0))
+        if (fps != VI.getFPS(index))
         {
             VI.stopDevice(index);
             VI.setIdealFramerate(index,fps);

modules/highgui/src/precomp.hpp

@@ -172,10 +172,12 @@ CvVideoWriter* cvCreateVideoWriter_GStreamer( const char* filename, int fourcc,
 void cvSetModeWindow_W32(const char* name, double prop_value);
 void cvSetModeWindow_GTK(const char* name, double prop_value);
 void cvSetModeWindow_CARBON(const char* name, double prop_value);
+void cvSetModeWindow_COCOA(const char* name, double prop_value);
 
 double cvGetModeWindow_W32(const char* name);
 double cvGetModeWindow_GTK(const char* name);
 double cvGetModeWindow_CARBON(const char* name);
+double cvGetModeWindow_COCOA(const char* name);
 
 double cvGetPropWindowAutoSize_W32(const char* name);
 double cvGetPropWindowAutoSize_GTK(const char* name);

modules/highgui/src/window.cpp

@@ -62,6 +62,8 @@ CV_IMPL void cvSetWindowProperty(const char* name, int prop_id, double prop_valu
             cvSetModeWindow_GTK(name,prop_value);
         #elif defined (HAVE_CARBON)
             cvSetModeWindow_CARBON(name,prop_value);
+        #elif defined (HAVE_COCOA)
+            cvSetModeWindow_COCOA(name,prop_value);
         #endif
     break;
 
@@ -99,6 +101,8 @@ CV_IMPL double cvGetWindowProperty(const char* name, int prop_id)
             return cvGetModeWindow_GTK(name);
         #elif defined (HAVE_CARBON)
             return cvGetModeWindow_CARBON(name);
+        #elif defined (HAVE_COCOA)
+            return cvGetModeWindow_COCOA(name);
         #else
             return -1;
         #endif

modules/highgui/src/window_cocoa.mm

@@ -109,13 +109,15 @@ static bool wasInitialized = false;
 	CvMouseCallback mouseCallback;
 	void *mouseParam;
 	BOOL autosize;
-	BOOL firstContent; 
+	BOOL firstContent;
+    int status;
 }
 @property(assign) CvMouseCallback mouseCallback;
 @property(assign) void *mouseParam;
 @property(assign) BOOL autosize;
 @property(assign) BOOL firstContent; 
 @property(retain) NSMutableDictionary *sliders;
+@property(readwrite) int status;
 - (CVView *)contentView;
 - (void)cvSendMouseEvent:(NSEvent *)event type:(int)type flags:(int)flags;
 - (void)cvMouseEvent:(NSEvent *)event;
@@ -533,6 +535,75 @@ CV_IMPL int cvWaitKey (int maxWait)
 	return returnCode;
 }
 
+double cvGetModeWindow_COCOA( const char* name )
+{
+    double result = -1;
+    CVWindow *window = nil;
+
+    CV_FUNCNAME( "cvGetModeWindow_COCOA" );
+
+    __BEGIN__;
+    if( name == NULL )
+    {
+        CV_ERROR( CV_StsNullPtr, "NULL name string" );
+    }
+
+    window = cvGetWindow( name );
+    if ( window == NULL )
+    {
+        CV_ERROR( CV_StsNullPtr, "NULL window" );
+    }
+
+    result = window.status;
+
+    __END__;
+    return result;
+}
+
+void cvSetModeWindow_COCOA( const char* name, double prop_value )
+{
+    CVWindow *window = nil;
+    NSDictionary *fullscreenOptions = nil;
+    NSAutoreleasePool* localpool = nil;
+
+    CV_FUNCNAME( "cvSetModeWindow_COCOA" );
+
+    __BEGIN__;
+    if( name == NULL )
+    {
+        CV_ERROR( CV_StsNullPtr, "NULL name string" );
+    }
+
+    window = cvGetWindow(name);
+    if ( window == NULL )
+    {
+        CV_ERROR( CV_StsNullPtr, "NULL window" );
+    }
+
+    if ( [window autosize] )
+    {
+        return;
+    }
+
+    localpool = [[NSAutoreleasePool alloc] init];
+
+    fullscreenOptions = [NSDictionary dictionaryWithObject:[NSNumber numberWithBool:YES] forKey:NSFullScreenModeSetting];
+    if ( [[window contentView] isInFullScreenMode] && prop_value==CV_WINDOW_NORMAL )
+    {
+        [[window contentView] exitFullScreenModeWithOptions:fullscreenOptions];
+        window.status=CV_WINDOW_NORMAL;
+    }
+    else if( ![[window contentView] isInFullScreenMode] && prop_value==CV_WINDOW_FULLSCREEN )
+    {
+        [[window contentView] enterFullScreenMode:[NSScreen mainScreen] withOptions:fullscreenOptions];
+        window.status=CV_WINDOW_FULLSCREEN;
+    }
+
+    [localpool drain];
+
+    __END__;
+}
+
 @implementation CVWindow 
 
 @synthesize mouseCallback;
@@ -540,6 +611,7 @@ CV_IMPL int cvWaitKey (int maxWait)
 @synthesize autosize;
 @synthesize firstContent; 
 @synthesize sliders;
+@synthesize status;
 
 - (void)cvSendMouseEvent:(NSEvent *)event type:(int)type flags:(int)flags {
 	//cout << "cvSendMouseEvent" << endl; 
@@ -787,9 +859,11 @@ CV_IMPL int cvWaitKey (int maxWait)
 	NSAutoreleasePool* localpool = [[NSAutoreleasePool alloc] init];
 	CVWindow *cvwindow = (CVWindow *)[self window];
 	int height = 0;
-	for(NSString *key in [cvwindow sliders]) {
-		height += [[[cvwindow sliders] valueForKey:key] frame].size.height;
-	}
+    if ([cvwindow respondsToSelector:@selector(sliders)]) {
+        for(NSString *key in [cvwindow sliders]) {
+            height += [[[cvwindow sliders] valueForKey:key] frame].size.height;
+        }
+    }
 	
 
 	NSRect imageRect = {{0,0}, {[image size].width, [image size].height}};

modules/java/CMakeLists.txt

@@ -151,14 +151,13 @@ add_custom_target(${api_target} DEPENDS ${java_files} ${documented_java_files} $
 add_library(${the_module} SHARED ${handwrittren_h_sources} ${handwrittren_cpp_sources} ${generated_cpp_sources})
 if(BUILD_FAT_JAVA_LIB)
   set(__deps ${OPENCV_MODULE_${the_module}_DEPS} ${OPENCV_MODULES_BUILD})
-  list(REMOVE_ITEM __deps ${the_module})
+  list(REMOVE_ITEM __deps ${the_module} opencv_ts)
   ocv_list_unique(__deps)
   set(__extradeps ${__deps})
   ocv_list_filterout(__extradeps "^opencv_")
   if(__extradeps)
     list(REMOVE_ITEM __deps ${__extradeps})
   endif()
-
   target_link_libraries(${the_module} -Wl,-whole-archive ${__deps} -Wl,-no-whole-archive ${__extradeps} ${OPENCV_LINKER_LIBS})
 else()
   target_link_libraries(${the_module} ${OPENCV_MODULE_${the_module}_DEPS} ${OPENCV_LINKER_LIBS})

modules/java/android_lib/.project

@@ -1,6 +1,6 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <projectDescription>
-	<name>OpenCV-2.4.2</name>
+	<name>OpenCV Library-2.4.2</name>
 	<comment></comment>
 	<projects>
 	</projects>

modules/ml/src/boost.cpp

@@ -1896,7 +1896,7 @@ void CvBoost::write_params( CvFileStorage* fs ) const
     else
         cvWriteInt( fs, "splitting_criteria", params.split_criteria );
 
-    cvWriteInt( fs, "ntrees", params.weak_count );
+	cvWriteInt( fs, "ntrees", weak->total );
     cvWriteReal( fs, "weight_trimming_rate", params.weight_trim_rate );
 
     data->write_params( fs );

modules/stitching/src/matchers.cpp

@@ -273,14 +273,14 @@ namespace cv {
 namespace detail {
 
 void FeaturesFinder::operator ()(const Mat &image, ImageFeatures &features)
-{ 
+{
     find(image, features);
     features.img_size = image.size();
 }
 
 
 void FeaturesFinder::operator ()(const Mat &image, ImageFeatures &features, const vector<Rect> &rois)
-{ 
+{
     vector<ImageFeatures> roi_features(rois.size());
     size_t total_kps_count = 0;
     int total_descriptors_height = 0;
@@ -294,8 +294,8 @@ void FeaturesFinder::operator ()(const Mat &image, ImageFeatures &features, cons
 
     features.img_size = image.size();
     features.keypoints.resize(total_kps_count);
-    features.descriptors.create(total_descriptors_height, 
-                                roi_features[0].descriptors.cols, 
+    features.descriptors.create(total_descriptors_height,
+                                roi_features[0].descriptors.cols,
                                 roi_features[0].descriptors.type());
 
     int kp_idx = 0;
@@ -332,14 +332,14 @@ SurfFeaturesFinder::SurfFeaturesFinder(double hess_thresh, int num_octaves, int
     {
         detector_ = Algorithm::create<FeatureDetector>("Feature2D.SURF");
         extractor_ = Algorithm::create<DescriptorExtractor>("Feature2D.SURF");
-        
+
         if( detector_.empty() || extractor_.empty() )
             CV_Error( CV_StsNotImplemented, "OpenCV was built without SURF support" );
-        
+
         detector_->set("hessianThreshold", hess_thresh);
         detector_->set("nOctaves", num_octaves);
         detector_->set("nOctaveLayers", num_layers);
-        
+
         extractor_->set("nOctaves", num_octaves_descr);
         extractor_->set("nOctaveLayers", num_layers_descr);
     }
@@ -403,17 +403,17 @@ void OrbFeaturesFinder::find(const Mat &image, ImageFeatures &features)
                 int xr = (c+1) * gray_image.cols / grid_size.width;
                 int yr = (r+1) * gray_image.rows / grid_size.height;
 
-                LOGLN("OrbFeaturesFinder::find: gray_image.empty=" << (gray_image.empty()?"true":"false") << ", "
-                    << " gray_image.size()=(" << gray_image.size().width << "x" << gray_image.size().height << "), "
-                    << " yl=" << yl << ", yr=" << yr << ", "
-                    << " xl=" << xl << ", xr=" << xr << ", gray_image.data=" << ((size_t)gray_image.data) << ", "
-                    << "gray_image.dims=" << gray_image.dims << "\n");
+                // LOGLN("OrbFeaturesFinder::find: gray_image.empty=" << (gray_image.empty()?"true":"false") << ", "
+                //     << " gray_image.size()=(" << gray_image.size().width << "x" << gray_image.size().height << "), "
+                //     << " yl=" << yl << ", yr=" << yr << ", "
+                //     << " xl=" << xl << ", xr=" << xr << ", gray_image.data=" << ((size_t)gray_image.data) << ", "
+                //     << "gray_image.dims=" << gray_image.dims << "\n");
 
                 Mat gray_image_part=gray_image(Range(yl, yr), Range(xl, xr));
-                LOGLN("OrbFeaturesFinder::find: gray_image_part.empty=" << (gray_image_part.empty()?"true":"false") << ", "
-                    << " gray_image_part.size()=(" << gray_image_part.size().width << "x" << gray_image_part.size().height << "), "
-                    << " gray_image_part.dims=" << gray_image_part.dims << ", "
-                    << " gray_image_part.data=" << ((size_t)gray_image_part.data) << "\n");
+                // LOGLN("OrbFeaturesFinder::find: gray_image_part.empty=" << (gray_image_part.empty()?"true":"false") << ", "
+                //     << " gray_image_part.size()=(" << gray_image_part.size().width << "x" << gray_image_part.size().height << "), "
+                //     << " gray_image_part.dims=" << gray_image_part.dims << ", "
+                //     << " gray_image_part.data=" << ((size_t)gray_image_part.data) << "\n");
 
                 (*orb)(gray_image_part, Mat(), points, descriptors);
 
@@ -583,11 +583,11 @@ void BestOf2NearestMatcher::match(const ImageFeatures &features1, const ImageFea
         if (matches_info.inliers_mask[i])
             matches_info.num_inliers++;
 
-    // These coeffs are from paper M. Brown and D. Lowe. "Automatic Panoramic Image Stitching 
+    // These coeffs are from paper M. Brown and D. Lowe. "Automatic Panoramic Image Stitching
     // using Invariant Features"
     matches_info.confidence = matches_info.num_inliers / (8 + 0.3 * matches_info.matches.size());
 
-    // Set zero confidence to remove matches between too close images, as they don't provide 
+    // Set zero confidence to remove matches between too close images, as they don't provide
     // additional information anyway. The threshold was set experimentally.
     matches_info.confidence = matches_info.confidence > 3. ? 0. : matches_info.confidence;
 

modules/video/src/precomp.hpp

@@ -52,6 +52,8 @@
 #include "opencv2/imgproc/imgproc_c.h"
 #include "opencv2/core/internal.hpp"
 
+#include <list>
+
 #ifdef HAVE_TEGRA_OPTIMIZATION
 #include "opencv2/video/video_tegra.hpp"
 #endif

samples/android/15-puzzle/.project

@@ -1,33 +1,33 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<projectDescription>
-	<name>Sample - 15-puzzle</name>
-	<comment></comment>
-	<projects>
-	</projects>
-	<buildSpec>
-		<buildCommand>
-			<name>com.android.ide.eclipse.adt.ResourceManagerBuilder</name>
-			<arguments>
-			</arguments>
-		</buildCommand>
-		<buildCommand>
-			<name>com.android.ide.eclipse.adt.PreCompilerBuilder</name>
-			<arguments>
-			</arguments>
-		</buildCommand>
-		<buildCommand>
-			<name>org.eclipse.jdt.core.javabuilder</name>
-			<arguments>
-			</arguments>
-		</buildCommand>
-		<buildCommand>
-			<name>com.android.ide.eclipse.adt.ApkBuilder</name>
-			<arguments>
-			</arguments>
-		</buildCommand>
-	</buildSpec>
-	<natures>
-		<nature>com.android.ide.eclipse.adt.AndroidNature</nature>
-		<nature>org.eclipse.jdt.core.javanature</nature>
-	</natures>
-</projectDescription>
+<?xml version="1.0" encoding="UTF-8"?>
+<projectDescription>
+	<name>OpenCV Sample - 15-puzzle</name>
+	<comment></comment>
+	<projects>
+	</projects>
+	<buildSpec>
+		<buildCommand>
+			<name>com.android.ide.eclipse.adt.ResourceManagerBuilder</name>
+			<arguments>
+			</arguments>
+		</buildCommand>
+		<buildCommand>
+			<name>com.android.ide.eclipse.adt.PreCompilerBuilder</name>
+			<arguments>
+			</arguments>
+		</buildCommand>
+		<buildCommand>
+			<name>org.eclipse.jdt.core.javabuilder</name>
+			<arguments>
+			</arguments>
+		</buildCommand>
+		<buildCommand>
+			<name>com.android.ide.eclipse.adt.ApkBuilder</name>
+			<arguments>
+			</arguments>
+		</buildCommand>
+	</buildSpec>
+	<natures>
+		<nature>com.android.ide.eclipse.adt.AndroidNature</nature>
+		<nature>org.eclipse.jdt.core.javanature</nature>
+	</natures>
+</projectDescription>

samples/android/15-puzzle/res/values/strings.xml

@@ -1,4 +1,4 @@
 <?xml version="1.0" encoding="utf-8"?>
 <resources>
-    <string name="app_name">OpenCV Sample - 15-puzzle</string>
+    <string name="app_name">OCV 15 Puzzle</string>
 </resources>

samples/android/color-blob-detection/.project

@@ -1,6 +1,6 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <projectDescription>
-	<name>ColorBlobDetection</name>
+	<name>OpenCV Sample - color-blob-detection</name>
 	<comment></comment>
 	<projects>
 	</projects>

samples/android/color-blob-detection/AndroidManifest.xml

@@ -7,7 +7,7 @@
     <uses-sdk android:minSdkVersion="8" />
 
     <application
-        android:icon="@drawable/ic_launcher"
+        android:icon="@drawable/icon"
         android:label="@string/app_name" >
         <activity
             android:name="org.opencv.samples.colorblobdetect.ColorBlobDetectionActivity"

samples/android/color-blob-detection/res/layout/main.xml

@@ -1,12 +0,0 @@
-<?xml version="1.0" encoding="utf-8"?>
-<LinearLayout xmlns:android="http://schemas.android.com/apk/res/android"
-    android:layout_width="fill_parent"
-    android:layout_height="fill_parent"
-    android:orientation="vertical" >
-
-    <TextView
-        android:layout_width="fill_parent"
-        android:layout_height="wrap_content"
-        android:text="@string/hello" />
-
-</LinearLayout>

samples/android/color-blob-detection/res/values/strings.xml

@@ -1,7 +1,4 @@
 <?xml version="1.0" encoding="utf-8"?>
 <resources>
-
-    <string name="hello">Hello World, ColorBlobDetectionActivity!</string>
-    <string name="app_name">ColorBlobDetection</string>
-
+    <string name="app_name">OCV Color Blob Detection</string>
 </resources>

samples/android/face-detection/.project

@@ -1,6 +1,6 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <projectDescription>
-	<name>Sample - face-detection</name>
+	<name>OpenCV Sample - face-detection</name>
 	<comment></comment>
 	<projects>
 	</projects>

samples/android/face-detection/res/values/strings.xml

@@ -1,4 +1,4 @@
 <?xml version="1.0" encoding="utf-8"?>
 <resources>
-    <string name="app_name">OpenCV Sample - face-detection</string>
+    <string name="app_name">OCV Face Detection</string>
 </resources>

samples/android/image-manipulations/.project

@@ -1,6 +1,6 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <projectDescription>
-	<name>Sample - image-manipulations</name>
+	<name>OpenCV Sample - image-manipulations</name>
 	<comment></comment>
 	<projects>
 	</projects>

samples/android/image-manipulations/res/values/strings.xml

@@ -1,4 +1,4 @@
 <?xml version="1.0" encoding="utf-8"?>
 <resources>
-    <string name="app_name">OpenCV Sample - image-manipulations</string>
+    <string name="app_name">OCV Image Manipulations</string>
 </resources>

samples/android/tutorial-0-androidcamera/.project

@@ -1,33 +1,33 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<projectDescription>
-	<name>Tutorial 0 (Basic) - Android Camera</name>
-	<comment></comment>
-	<projects>
-	</projects>
-	<buildSpec>
-		<buildCommand>
-			<name>com.android.ide.eclipse.adt.ResourceManagerBuilder</name>
-			<arguments>
-			</arguments>
-		</buildCommand>
-		<buildCommand>
-			<name>com.android.ide.eclipse.adt.PreCompilerBuilder</name>
-			<arguments>
-			</arguments>
-		</buildCommand>
-		<buildCommand>
-			<name>org.eclipse.jdt.core.javabuilder</name>
-			<arguments>
-			</arguments>
-		</buildCommand>
-		<buildCommand>
-			<name>com.android.ide.eclipse.adt.ApkBuilder</name>
-			<arguments>
-			</arguments>
-		</buildCommand>
-	</buildSpec>
-	<natures>
-		<nature>com.android.ide.eclipse.adt.AndroidNature</nature>
-		<nature>org.eclipse.jdt.core.javanature</nature>
-	</natures>
-</projectDescription>
+<?xml version="1.0" encoding="UTF-8"?>
+<projectDescription>
+	<name>OpenCV Tutorial 0 - Android Camera</name>
+	<comment></comment>
+	<projects>
+	</projects>
+	<buildSpec>
+		<buildCommand>
+			<name>com.android.ide.eclipse.adt.ResourceManagerBuilder</name>
+			<arguments>
+			</arguments>
+		</buildCommand>
+		<buildCommand>
+			<name>com.android.ide.eclipse.adt.PreCompilerBuilder</name>
+			<arguments>
+			</arguments>
+		</buildCommand>
+		<buildCommand>
+			<name>org.eclipse.jdt.core.javabuilder</name>
+			<arguments>
+			</arguments>
+		</buildCommand>
+		<buildCommand>
+			<name>com.android.ide.eclipse.adt.ApkBuilder</name>
+			<arguments>
+			</arguments>
+		</buildCommand>
+	</buildSpec>
+	<natures>
+		<nature>com.android.ide.eclipse.adt.AndroidNature</nature>
+		<nature>org.eclipse.jdt.core.javanature</nature>
+	</natures>
+</projectDescription>

samples/android/tutorial-0-androidcamera/res/values/strings.xml

@@ -1,4 +1,4 @@
 <?xml version="1.0" encoding="utf-8"?>
 <resources>
-    <string name="app_name">Tutorial 0 (Basic) - Android Camera</string>
+    <string name="app_name">OCV T0 Android Camera</string>
 </resources>

samples/android/tutorial-1-addopencv/.project

@@ -1,6 +1,6 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <projectDescription>
-	<name>Tutorial 1 (Basic) - Add OpenCV</name>
+	<name>OpenCV Tutorial 1 - Add OpenCV</name>
 	<comment></comment>
 	<projects>
 	</projects>

samples/android/tutorial-1-addopencv/res/values/strings.xml

@@ -1,4 +1,4 @@
 <?xml version="1.0" encoding="utf-8"?>
 <resources>
-    <string name="app_name">Tutorial 1 (Basic) - Add OpenCV</string>
+    <string name="app_name">OCV T1 Add OpenCV</string>
 </resources>

samples/android/tutorial-2-opencvcamera/.project

@@ -1,6 +1,6 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <projectDescription>
-	<name>Tutorial 2 (Basic) - Use OpenCV Camera</name>
+	<name>OpenCV Tutorial 2 - Use OpenCV Camera</name>
 	<comment></comment>
 	<projects>
 	</projects>

samples/android/tutorial-2-opencvcamera/res/values/strings.xml

@@ -1,4 +1,4 @@
 <?xml version="1.0" encoding="utf-8"?>
 <resources>
-    <string name="app_name">Tutorial 2 (Basic) - Use OpenCV Camera</string>
+    <string name="app_name">OCV T2 Use OpenCV Camera</string>
 </resources>

samples/android/tutorial-3-native/.project

@@ -1,6 +1,6 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <projectDescription>
-	<name>Tutorial 3 (Advanced) - Add Native OpenCV</name>
+	<name>OpenCV Tutorial 3 - Add Native OpenCV</name>
 	<comment></comment>
 	<projects>
 	</projects>

samples/android/tutorial-3-native/res/values/strings.xml

@@ -1,4 +1,4 @@
 <?xml version="1.0" encoding="utf-8"?>
 <resources>
-    <string name="app_name">Tutorial 3 (Advanced) - Add Native OpenCV</string>
+    <string name="app_name">OCV T3 Add Native OpenCV</string>
 </resources>

samples/android/tutorial-4-mixed/.project

@@ -1,6 +1,6 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <projectDescription>
-	<name>Tutorial 4 (Advanced) - Mix Java+Native OpenCV</name>
+	<name>OpenCV Tutorial 4 - Mix Java+Native OpenCV</name>
 	<comment></comment>
 	<projects>
 	</projects>

samples/android/tutorial-4-mixed/res/values/strings.xml

@@ -1,4 +1,4 @@
 <?xml version="1.0" encoding="utf-8"?>
 <resources>
-    <string name="app_name">Tutorial 4 (Advanced) - Mix Java+Native OpenCV</string>
+    <string name="app_name">OCV T4 Mix Java+Native OpenCV</string>
 </resources>

samples/cpp/bagofwords_classification.cpp

@@ -2014,7 +2014,7 @@ struct VocabTrainParams
                           // It shouldn't matter which object class is specified here - visual vocab will still be the same.
     int vocabSize; //number of visual words in vocabulary to train
     int memoryUse; // Memory to preallocate (in MB) when training vocab.
-                      // Change this depending on the size of the dataset/available memory.
+                   // Change this depending on the size of the dataset/available memory.
     float descProportion; // Specifies the number of descriptors to use from each image as a proportion of the total num descs.
 };
 
@@ -2126,8 +2126,10 @@ static Mat trainVocabulary( const string& filename, VocData& vocData, const Voca
     if( !readVocabulary( filename, vocabulary) )
     {
         CV_Assert( dextractor->descriptorType() == CV_32FC1 );
-        const int descByteSize = dextractor->descriptorSize()*4;
-        const int maxDescCount = (trainParams.memoryUse * 1048576) / descByteSize; // Total number of descs to use for training.
+        const int elemSize = CV_ELEM_SIZE(dextractor->descriptorType());
+        const int descByteSize = dextractor->descriptorSize() * elemSize;
+        const int bytesInMB = 1048576;
+        const int maxDescCount = (trainParams.memoryUse * bytesInMB) / descByteSize; // Total number of descs to use for training.
 
         cout << "Extracting VOC data..." << endl;
         vector<ObdImage> images;
@@ -2142,9 +2144,8 @@ static Mat trainVocabulary( const string& filename, VocData& vocData, const Voca
 
         while( images.size() > 0 )
         {
-            if( bowTrainer.descripotorsCount() >= maxDescCount )
+            if( bowTrainer.descripotorsCount() > maxDescCount )
             {
-                assert( bowTrainer.descripotorsCount() == maxDescCount );
 #ifdef DEBUG_DESC_PROGRESS
                 cout << "Breaking due to full memory ( descriptors count = " << bowTrainer.descripotorsCount()
                         << "; descriptor size in bytes = " << descByteSize << "; all used memory = "

samples/python2/digits.py

@@ -17,6 +17,7 @@ from common import clock, mosaic
 
 SZ = 20 # size of each digit is SZ x SZ
 CLASS_N = 10
+DIGITS_FN = 'digits.png'
 
 def load_digits(fn):
     print 'loading "%s" ...' % fn
@@ -95,7 +96,7 @@ def evaluate_model(model, digits, samples, labels):
 if __name__ == '__main__':
     print __doc__
 
-    digits, labels = load_digits('digits.png')
+    digits, labels = load_digits(DIGITS_FN)
     
     print 'preprocessing...'
     # shuffle digits

samples/python2/digits_adjust.py

@@ -11,11 +11,10 @@ Usage:
   digits_adjust.py [--model {svm|knearest}] [--cloud] [--env <PiCloud environment>]
   
   --model {svm|knearest}   - select the classifier (SVM is the default)
-  --cloud                  - use PiCloud computing platform (for SVM only)
+  --cloud                  - use PiCloud computing platform
   --env                    - cloud environment name
 
 '''
-# TODO dataset preprocessing in cloud
 # TODO cloud env setup tutorial
 
 import numpy as np
@@ -24,6 +23,14 @@ from multiprocessing.pool import ThreadPool
 
 from digits import *
 
+try: 
+    import cloud
+    have_cloud = True
+except ImportError:
+    have_cloud = False
+    
+
+
 def cross_validate(model_class, params, samples, labels, kfold = 3, pool = None):
     n = len(samples)
     folds = np.array_split(np.arange(n), kfold)
@@ -46,66 +53,88 @@ def cross_validate(model_class, params, samples, labels, kfold = 3, pool = None)
         scores = pool.map(f, xrange(kfold))
     return np.mean(scores)
 
-def adjust_KNearest(samples, labels):
-    print 'adjusting KNearest ...'
-    best_err, best_k = np.inf, -1
-    for k in xrange(1, 9):
-        err = cross_validate(KNearest, dict(k=k), samples, labels)
-        if err < best_err:
-            best_err, best_k = err, k
-        print 'k = %d, error: %.2f %%' % (k, err*100)
-    best_params = dict(k=best_k)
-    print 'best params:', best_params
-    return best_params
-
-def adjust_SVM(samples, labels, usecloud=False, cloud_env=''):
-    Cs = np.logspace(0, 5, 10, base=2)
-    gammas = np.logspace(-7, -2, 10, base=2)
-    scores = np.zeros((len(Cs), len(gammas)))
-    scores[:] = np.nan
-
-    if usecloud:
-        try: 
-            import cloud
-        except ImportError: 
-            print 'cloud module is not installed'
+
+class App(object):
+    def __init__(self, usecloud=False, cloud_env=''):
+        if usecloud and not have_cloud:
+            print 'warning: cloud module is not installed, running locally'
             usecloud = False
-    if usecloud:
-        print 'uploading dataset to cloud...'
-        np.savez('train.npz', samples=samples, labels=labels)
-        cloud.files.put('train.npz')
-
-    print 'adjusting SVM (may take a long time) ...'
-    def f(job):
-        i, j = job
-        params = dict(C = Cs[i], gamma=gammas[j])
-        score = cross_validate(SVM, params, samples, labels)
-        return i, j, score
-    def fcloud(job):
-        i, j = job
-        cloud.files.get('train.npz')
-        npz = np.load('train.npz')
-        params = dict(C = Cs[i], gamma=gammas[j])
-        score = cross_validate(SVM, params, npz['samples'], npz['labels'])
-        return i, j, score
-    
-    if usecloud:
-        jids = cloud.map(fcloud, np.ndindex(*scores.shape), _env=cloud_env, _profile=True)
-        ires = cloud.iresult(jids)
-    else:
-        pool = ThreadPool(processes=cv2.getNumberOfCPUs())
-        ires = pool.imap_unordered(f, np.ndindex(*scores.shape))
+        self.usecloud = usecloud
+        self.cloud_env = cloud_env
+
+        if self.usecloud:
+            print 'uploading dataset to cloud...'
+            cloud.files.put(DIGITS_FN)
+            self.preprocess_job = cloud.call(self.preprocess, _env=self.cloud_env)
+        else:
+            self._samples, self._labels = self.preprocess()
 
-    for count, (i, j, score) in enumerate(ires):
-        scores[i, j] = score
-        print '%d / %d (best error: %.2f %%, last: %.2f %%)' % (count+1, scores.size, np.nanmin(scores)*100, score*100)
-    print scores
+    def preprocess(self):
+        if self.usecloud:
+            cloud.files.get(DIGITS_FN)
+        digits, labels = load_digits(DIGITS_FN)
+        shuffle = np.random.permutation(len(digits))
+        digits, labels = digits[shuffle], labels[shuffle]
+        digits2 = map(deskew, digits)
+        samples = np.float32(digits2).reshape(-1, SZ*SZ) / 255.0
+        return samples, labels
+
+    def get_dataset(self):
+        if self.usecloud:
+            return cloud.result(self.preprocess_job)
+        else:
+            return self._samples, self._labels
+
+    def run_jobs(self, f, jobs):
+        if self.usecloud:
+            jids = cloud.map(f, jobs, _env=self.cloud_env, _profile=True, _depends_on=self.preprocess_job)
+            ires = cloud.iresult(jids)
+        else:
+            pool = ThreadPool(processes=cv2.getNumberOfCPUs())
+            ires = pool.imap_unordered(f, jobs)
+        return ires
+            
+    def adjust_SVM(self):
+        Cs = np.logspace(0, 5, 10, base=2)
+        gammas = np.logspace(-7, -2, 10, base=2)
+        scores = np.zeros((len(Cs), len(gammas)))
+        scores[:] = np.nan
+
+        print 'adjusting SVM (may take a long time) ...'
+        def f(job):
+            i, j = job
+            samples, labels = self.get_dataset()
+            params = dict(C = Cs[i], gamma=gammas[j])
+            score = cross_validate(SVM, params, samples, labels)
+            return i, j, score
+        
+        ires = self.run_jobs(f, np.ndindex(*scores.shape))
+        for count, (i, j, score) in enumerate(ires):
+            scores[i, j] = score
+            print '%d / %d (best error: %.2f %%, last: %.2f %%)' % (count+1, scores.size, np.nanmin(scores)*100, score*100)
+        print scores
+
+        i, j = np.unravel_index(scores.argmin(), scores.shape)
+        best_params = dict(C = Cs[i], gamma=gammas[j])
+        print 'best params:', best_params
+        print 'best error: %.2f %%' % (scores.min()*100)
+        return best_params
+
+    def adjust_KNearest(self):
+        print 'adjusting KNearest ...'
+        def f(k):
+            samples, labels = self.get_dataset()
+            err = cross_validate(KNearest, dict(k=k), samples, labels)
+            return k, err
+        best_err, best_k = np.inf, -1
+        for k, err in self.run_jobs(f, xrange(1, 9)):
+            if err < best_err:
+                best_err, best_k = err, k
+            print 'k = %d, error: %.2f %%' % (k, err*100)
+        best_params = dict(k=best_k)
+        print 'best params:', best_params, 'err: %.2f' % (best_err*100)
+        return best_params
 
-    i, j = np.unravel_index(scores.argmin(), scores.shape)
-    best_params = dict(C = Cs[i], gamma=gammas[j])
-    print 'best params:', best_params
-    print 'best error: %.2f %%' % (scores.min()*100)
-    return best_params
 
 if __name__ == '__main__':
     import getopt
@@ -113,6 +142,7 @@ if __name__ == '__main__':
     
     print __doc__
 
+
     args, _ = getopt.getopt(sys.argv[1:], '', ['model=', 'cloud', 'env='])
     args = dict(args)
     args.setdefault('--model', 'svm')
@@ -121,16 +151,10 @@ if __name__ == '__main__':
         print 'unknown model "%s"' % args['--model']
         sys.exit(1)
 
-    digits, labels = load_digits('digits.png')
-    shuffle = np.random.permutation(len(digits))
-    digits, labels = digits[shuffle], labels[shuffle]
-    digits2 = map(deskew, digits)
-    samples = np.float32(digits2).reshape(-1, SZ*SZ) / 255.0
-    
     t = clock()
+    app = App(usecloud='--cloud' in args, cloud_env = args['--env'])
     if args['--model'] == 'knearest':
-        adjust_KNearest(samples, labels)
+        app.adjust_KNearest()
     else:
-        adjust_SVM(samples, labels, usecloud='--cloud' in args, cloud_env = args['--env'])
+        app.adjust_SVM()
     print 'work time: %f s' % (clock() - t)
-        

samples/python2/fitline.py

@@ -0,0 +1,78 @@
+'''
+Robust line fitting.
+==================
+
+Example of using cv2.fitLine function for fitting line to points in presence of outliers.
+
+Usage
+-----
+fitline.py
+
+Switch through different M-estimator functions and see, how well the robust functions
+fit the line even in case of ~50% of outliers.
+
+Keys
+----
+SPACE - generaty random points
+f     - change distance function
+ESC   - exit
+'''
+
+import numpy as np
+import cv2
+import itertools as it
+from common import draw_str
+
+
+w, h = 512, 256
+
+def toint(p):
+    return tuple(map(int, p))
+
+def sample_line(p1, p2, n, noise=0.0):
+    p1 = np.float32(p1)
+    t = np.random.rand(n,1)
+    return p1 + (p2-p1)*t + np.random.normal(size=(n, 2))*noise
+
+dist_func_names = it.cycle('CV_DIST_L2 CV_DIST_L1 CV_DIST_L12 CV_DIST_FAIR CV_DIST_WELSCH CV_DIST_HUBER'.split())
+cur_func_name = dist_func_names.next()
+
+def update(_=None):
+    noise = cv2.getTrackbarPos('noise', 'fit line')
+    n = cv2.getTrackbarPos('point n', 'fit line')
+    r = cv2.getTrackbarPos('outlier %', 'fit line') / 100.0
+    outn = int(n*r)
+
+    p0, p1 = (90, 80), (w-90, h-80)
+    img = np.zeros((h, w, 3), np.uint8)
+    cv2.line(img, toint(p0), toint(p1), (0, 255, 0))
+
+    if n > 0:
+        line_points = sample_line(p0, p1, n-outn, noise)
+        outliers = np.random.rand(outn, 2) * (w, h)
+        points = np.vstack([line_points, outliers])
+        for p in line_points:
+            cv2.circle(img, toint(p), 2, (255, 255, 255), -1)
+        for p in outliers:
+            cv2.circle(img, toint(p), 2, (64, 64, 255), -1)
+        func = getattr(cv2.cv, cur_func_name)
+        vx, vy, cx, cy = cv2.fitLine(np.float32(points), func, 0, 0.01, 0.01)
+        cv2.line(img, (int(cx-vx*w), int(cy-vy*w)), (int(cx+vx*w), int(cy+vy*w)), (0, 0, 255))
+
+    draw_str(img, (20, 20), cur_func_name)
+    cv2.imshow('fit line', img)
+
+if __name__ == '__main__':
+    print __doc__
+
+    cv2.namedWindow('fit line')
+    cv2.createTrackbar('noise', 'fit line', 3, 50, update)
+    cv2.createTrackbar('point n', 'fit line', 100, 500, update)
+    cv2.createTrackbar('outlier %', 'fit line', 30, 100, update)
+    while True:
+        update()
+        ch = cv2.waitKey(0)
+        if ch == ord('f'):
+            cur_func_name = dist_func_names.next()
+        if ch == 27:
+            break

samples/python2/video.py

@@ -1,3 +1,32 @@
+'''
+Video capture sample.
+
+Sample shows how VideoCapture class can be used to acquire video
+frames from a camera of a movie file. Also the sample provides 
+an example of procedural video generation by an object, mimicking 
+the VideoCapture interface (see Chess class). 
+
+'create_capture' is a convinience function for capture creation, 
+falling back to procedural video in case of error.
+
+Usage:
+    video.py [--shotdir <shot path>] [source0] [source1] ...'
+
+    sourceN is an
+     - integer number for camera capture
+     - name of video file
+     - synth:<params> for procedural video
+
+Synth examples:
+    synth:bg=../cpp/lena.jpg:noise=0.1
+    synth:class=chess:bg=../cpp/lena.jpg:noise=0.1:size=640x480
+
+Keys:
+    ESC    - exit
+    SPACE  - save current frame to <shot path> directory
+
+'''
+
 import numpy as np
 import cv2
 from time import clock
@@ -100,8 +129,7 @@ presets = dict(
 
 
 def create_capture(source = 0, fallback = presets['chess']):
-    '''
-      source: <int> or '<int>|<filename>|synth [:<param_name>=<value> [:...]]'
+    '''source: <int> or '<int>|<filename>|synth [:<param_name>=<value> [:...]]'
     '''
     source = str(source).strip()
     chunks = source.split(':')
@@ -136,9 +164,7 @@ if __name__ == '__main__':
     import sys
     import getopt
 
-    print 'USAGE: video.py [--shotdir <dir>] [source0] [source1] ...'
-    print "source: '<int>' or '<filename>' or 'synth:<params>'"
-    print
+    print __doc__
 
     args, sources = getopt.getopt(sys.argv[1:], '', 'shotdir=')
     args = dict(args)
@@ -146,8 +172,6 @@ if __name__ == '__main__':
     if len(sources) == 0:
         sources = [ 0 ]
 
-    print 'Press SPACE to save current frame'
-
     caps = map(create_capture, sources)
     shot_idx = 0
     while True:

samples/python2/watershed.py

@@ -1,18 +1,31 @@
-import numpy as np
-import cv2
-from common import Sketcher
+'''
+Watershed segmentation
+=========
+
+This program demonstrates the watershed segmentation algorithm 
+in OpenCV: watershed().
 
-help_message = '''
-  USAGE: watershed.py [<image>]
+Usage
+-----
+watershed.py [image filename]
 
-  Use keys 1 - 7 to switch marker color
+Keys
+----
+  1-7   - switch marker color
   SPACE - update segmentation
   r     - reset
-  a     - switch autoupdate
+  a     - toggle autoupdate
   ESC   - exit
 
 '''
 
+
+
+
+import numpy as np
+import cv2
+from common import Sketcher
+
 class App:
     def __init__(self, fn):
         self.img = cv2.imread(fn)
@@ -60,5 +73,5 @@ if __name__ == '__main__':
     import sys
     try: fn = sys.argv[1]
     except: fn = '../cpp/fruits.jpg'
-    print help_message
+    print __doc__
     App(fn).run()