support RNG, TermCriteria

Tracking/classes.pl

@@ -30,7 +30,7 @@
 The class can calculate an optical flow for a sparse feature set using the
 iterative Lucas-Kanade method with pyramids.
 
-@sa calcOpticalFlowPyrLK',1,'cv::SparsePyrLKOpticalFlow::create',[[[],'']]],
+@sa calcOpticalFlowPyrLK',1,'cv::SparsePyrLKOpticalFlow::create',[[[['Size','winSize','Size(21, 21)',[]],['int','maxLevel','3',[]],['TermCriteria','crit','TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 0.01)',[]],['int','flags','0',[]],['double','minEigThreshold','1e-4',[]]],'']]],
 ['Tracker',[],'@brief Base abstract class for the long-term tracker'],
 ['TrackerMIL',['Tracker'],'@brief The MIL algorithm trains a classifier in an online manner to separate the object from the
 background.

classes.pl

@@ -33,6 +33,30 @@
 
 query descriptor index, train descriptor index, train image index, and distance between
 descriptors.',0,'cv::DMatch',[[[],''],[[['int','_queryIdx','',[]],['int','_trainIdx','',[]],['float','_distance','',[]]],''],[[['int','_queryIdx','',[]],['int','_trainIdx','',[]],['int','_imgIdx','',[]],['float','_distance','',[]]],'']]],
+['TermCriteria',[],'@brief The class defining termination criteria for iterative algorithms.
+
+You can initialize it by default constructor and then override any parameters, or the structure may
+be fully initialized using the advanced variant of the constructor.',0,'cv::TermCriteria',[[[],''],[[['int','type','',[]],['int','maxCount','',[]],['double','epsilon','',[]]],'@param type The type of termination criteria, one of TermCriteria::Type
+    @param maxCount The maximum number of iterations or elements to compute.
+    @param epsilon The desired accuracy or change in parameters at which the iterative algorithm stops.']]],
+['RNG',[],'@brief Random Number Generator
+
+Random number generator. It encapsulates the state (currently, a 64-bit
+integer) and has methods to return scalar random values and to fill
+arrays with random values. Currently it supports uniform and Gaussian
+(normal) distributions. The generator uses Multiply-With-Carry
+algorithm, introduced by G. Marsaglia (
+<http://en.wikipedia.org/wiki/Multiply-with-carry> ).
+Gaussian-distribution random numbers are generated using the Ziggurat
+algorithm ( <http://en.wikipedia.org/wiki/Ziggurat_algorithm> ),
+introduced by G. Marsaglia and W. W. Tsang.',0,'cv::RNG',[[[],'@brief constructor
+
+    These are the RNG constructors. The first form sets the state to some
+    pre-defined value, equal to 2\\*\\*32-1 in the current implementation. The
+    second form sets the state to the specified value. If you passed state=0
+    , the constructor uses the above default value instead to avoid the
+    singular random number sequence, consisting of all zeros.'],[[['uint64','state','',[]]],'@overload
+    @param state 64-bit value used to initialize the RNG.']]],
 ['Algorithm',[],'@brief This is a base class for all more or less complex algorithms in OpenCV
 
 especially for classes of algorithms, for which there can be multiple implementations. The examples

funclist.pl

@@ -133,6 +133,39 @@
  @param src input array or vector of matrices. all of the matrices must have the same number of rows and the same depth.
  @param dst output array. It has the same number of rows and depth as the src, and the sum of cols of the src.
 same depth.',0,'void',['vector_Mat','src','',[]],['Mat','dst','',['/O']]],
+['RNG','fill','@brief Fills arrays with random numbers.
+
+    @param mat 2D or N-dimensional matrix; currently matrices with more than
+    4 channels are not supported by the methods, use Mat::reshape as a
+    possible workaround.
+    @param distType distribution type, RNG::UNIFORM or RNG::NORMAL.
+    @param a first distribution parameter; in case of the uniform
+    distribution, this is an inclusive lower boundary, in case of the normal
+    distribution, this is a mean value.
+    @param b second distribution parameter; in case of the uniform
+    distribution, this is a non-inclusive upper boundary, in case of the
+    normal distribution, this is a standard deviation (diagonal of the
+    standard deviation matrix or the full standard deviation matrix).
+    @param saturateRange pre-saturation flag; for uniform distribution only;
+    if true, the method will first convert a and b to the acceptable value
+    range (according to the mat datatype) and then will generate uniformly
+    distributed random numbers within the range [saturate(a), saturate(b)),
+    if saturateRange=false, the method will generate uniformly distributed
+    random numbers in the original range [a, b) and then will saturate them,
+    it means, for example, that
+    <tt>theRNG().fill(mat_8u, RNG::UNIFORM, -DBL_MAX, DBL_MAX)</tt> will likely
+    produce array mostly filled with 0\'s and 255\'s, since the range (0, 255)
+    is significantly smaller than [-DBL_MAX, DBL_MAX).
+
+    Each of the methods fills the matrix with the random values from the
+    specified distribution. As the new numbers are generated, the RNG state
+    is updated accordingly. In case of multiple-channel images, every
+    channel is filled independently, which means that RNG cannot generate
+    samples from the multi-dimensional Gaussian distribution with
+    non-diagonal covariance matrix directly. To do that, the method
+    generates samples from multi-dimensional standard Gaussian distribution
+    with zero mean and identity covariation matrix, and then transforms them
+    using transform to get samples from the specified Gaussian distribution.',1,'void',['Mat','mat','',['/IO']],['int','distType','',[]],['Mat','a','',[]],['Mat','b','',[]],['bool','saturateRange','false',[]]],
 ['Algorithm','write','@brief simplified API for language bindings
     * @overload',1,'void',['Ptr_FileStorage','fs','',['/C','/Ref']],['String','name','String()',['/C','/Ref']]],
 ['Algorithm','read','@brief Reads algorithm parameters from a file storage',1,'void',['FileNode','fn','',['/C','/Ref']]],

genpp.pl

@@ -207,6 +207,7 @@ sub default_pl {
   $d =~ s/^($dimtypes_re)\(\)$/"empty($REALCTYPE2PDLTYPE{$DIMTYPES{$1}[0][0]})"/e;
   $d =~ s/^($dimtypes_re)(\(.*\))$/$REALCTYPE2PDLTYPE{$DIMTYPES{$1}[0][0]}.$2/e;
   $d =~ s/([A-Za-z0-9_:]+::[A-Za-z_][A-Za-z0-9_]+)/PDL::OpenCV::$1()/g;
+  $d =~ s/^(TermCriteria)(\(.*\))$/"PDL::OpenCV::$1->new".(length $2 == 2 ? '' : '2').$2/e;
   length $d ? "\$$self->{name} = $d if !defined \$$self->{name};" : ();
 }
 sub xs_par {

maint/genlists

@@ -25,6 +25,15 @@ my @HEADERS_FILES = do { local @ARGV = $HEADERS; grep !/^\s*#/, <> };
 chomp @HEADERS_FILES;
 
 my $tdir = tempdir(CLEANUP=>1);
+my %force_wrap = (
+  RotatedRect => [['.*angle.*']],
+  RNG => [['.*']],
+  TermCriteria => [['.*']],
+);
+my $class_re = join '|', keys %force_wrap;
+my $cons_re = join '|',
+  map "$_\\((?:".join('|', '', @{$force_wrap{$_}[0]}).")\\)", # always default
+  keys %force_wrap;
 sub process_header {
   my ($dir, $file) = @_;
   open my $fh, '<', catfile($dir, $file) or die "$file: $!";
@@ -33,9 +42,9 @@ sub process_header {
   make_path $outdir or die "$outdir: $!" if !-d $outdir;
   open my $outfh, '>', $outfile or die "$outfile: $!";
   my $intext = do { local $/; <$fh> };
-  $intext =~ s/(class\s+)(CV_EXPORTS)(\s+RotatedRect)/$1${2}_W$3/;
-  $intext =~ s/^(\s*)(RotatedRect\((?:.*angle|\)))/${1}CV_WRAP $2/gm;
-  $intext =~ s/^(\s*)(.*?boundingRect\()/${1}CV_WRAP $2/m;
+  $intext =~ s/(class\s+)(CV_EXPORTS)(\s+(?:$class_re))/$1${2}_W$3/g;
+  $intext =~ s/^(\s*)($cons_re)/${1}CV_WRAP $2/gm;
+  $intext =~ s/^(\s*)((?:.*?boundingRect|void\s+fill)\()/${1}CV_WRAP $2/gm;
   print $outfh $intext;
   $outfile;
 }
@@ -56,6 +65,7 @@ my %force = map +($_=>1), qw(
   cv.sum
   cv.wrapperEMD
   cv.GeneralizedHough.detect
+  cv.RNG.fill
   cv.Algorithm.read
   cv.Algorithm.write
   cv.FileNode.keys
@@ -121,7 +131,6 @@ my %class = map +($_=>1), qw(
   HOGDescriptor SparsePyrLKOpticalFlow DISOpticalFlow SparseOpticalFlow
   TermCriteria RNG RotatedRect
 );
-my $no_want = qr/^(TermCriteria|RNG)/;
 my $skip_re = join '|',
   qr/^cv\.ipp/,
   qr/^cv\.moments/,
@@ -171,7 +180,7 @@ print "class: $cname\n";
       die "Already got create $cname, now $name" if $dest2class2create{$dest}{$cname};
 print "class::create: $cname\n";
       (my $colons = $name) =~ s/\./::/g;
-      $args = [] if (@$args == 1 and $args->[0][0] =~ /Params/) or grep $_->[0] =~ /$no_want/, @$args; # temporary
+      $args = [] if @$args == 1 and $args->[0][0] =~ /Params/; # temporary
       $dest2class2create{$dest}{$cname} = [1, $colons, [[$args, $doc]]];
     } elsif ($ret =~ /^Ptr_/ and $name =~ /^cv\.(create(.*))/) {
       my ($full, $cname) = ($1, $2);
@@ -184,8 +193,6 @@ print "class.class: $cname @{[0+@$args]}\n";
       $dest2class2create{$dest}{$cname} ||= [0, "cv::$cname", []];
       push @{$dest2class2create{$dest}{$cname}[2]}, [$args, $doc];
     } elsif ($inc_all or $force{$name}) {
-      my @foundbad = map /$no_want/, $ret, map $_->[0], @$args;
-      print(" skipping a $name, found (@foundbad)\n"), next if @foundbad;
       my (undef, @n) = split /\./, $name;
       unshift @n, '' if @n == 1;
       die "Unexpected number of elts in (@n)" if @n != 2;

t/opencv_basic.t

@@ -7,6 +7,7 @@ use PDL::NiceSlice;
 use PDL::OpenCV;
 use PDL::OpenCV::Imgproc;
 use PDL::OpenCV::Imgcodecs;
+use PDL::OpenCV::Tracking;
 
 my $data = (xvals(5,8,3)+10*yvals(5,8,3)+zvals(1,1,3))->mv(2,0);
 my $slice = float $data(0);
@@ -23,6 +24,7 @@ my $pic2 = $pic->glue(1,$pic);
 is hconcat([$pic, $pic2])->dim(1), $pic->dim(1)*3, 'hconcat array-ref worked';
 is +(sumElems($data))[0]->sumover, 4560, 'sumElems';
 isa_ok +PDL::OpenCV::CLAHE->new, 'PDL::OpenCV::CLAHE', 'Size default OK';
+isa_ok +PDL::OpenCV::SparsePyrLKOpticalFlow->new, 'PDL::OpenCV::SparsePyrLKOpticalFlow', 'TermCriteria default OK';
 
 {
 my $a = pdl float, q[[1 1] [1 2] [0 3] [0 4] [1 5]];

typemap

@@ -7,6 +7,8 @@ vector_MatWrapper* T_OPENCV_VECTOR
 vector_StringWrapper* T_OPENCV_VECTOR
 vector_vector_Point2fWrapper* T_OPENCV_VECTOR
 RotatedRectWrapper* T_PTROBJ_SPECIAL
+RNGWrapper* T_PTROBJ_SPECIAL
+TermCriteriaWrapper* T_PTROBJ_SPECIAL
 KeyPointWrapper* T_PTROBJ_SPECIAL
 DMatchWrapper* T_PTROBJ_SPECIAL
 FileStorageWrapper* T_PTROBJ_SPECIAL