Cv

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Cv.xs  view on Meta::CPAN

# ============================================================

MODULE = Cv	PACKAGE = Cv
# ====================
CvHaarClassifierCascade*
cvLoadHaarClassifierCascade(const char* directory, CvSize orig_window_size)


MODULE = Cv	PACKAGE = Cv::Arr
CvSeq*
cvHaarDetectObjects(const CvArr* image, CvHaarClassifierCascade* cascade, CvMemStorage* storage, double scaleFactor=1.1, int minNeighbors=3, int flags=0, CvSize minSize=cvSize(0,0), CvSize maxSize=cvSize(0,0))
CODE:
	RETVAL = cvHaarDetectObjects(image, cascade, storage, scaleFactor, minNeighbors, flags, minSize
#if _CV_VERSION() >= _VERSION(2,2,0)
	, maxSize
#endif
	);
OUTPUT:
	RETVAL

MODULE = Cv	PACKAGE = Cv::HaarClassifierCascade
void
cvSetImagesForHaarClassifierCascade(CvHaarClassifierCascade* cascade, const CvArr* sum, const CvArr* sqsum, const CvArr* tilted_sum, double scale)

void
cvReleaseHaarClassifierCascade(CvHaarClassifierCascade* &cascade)
ALIAS: DESTROY = 1
INIT:
	unbless(ST(0));

int
cvRunHaarClassifierCascade(CvHaarClassifierCascade* cascade, CvPoint pt, int start_stage=0)


# ============================================================
#  video. Video Analysis: Motion Analysis and Object Tracking
# ============================================================

MODULE = Cv	PACKAGE = Cv::Arr
# ====================
double
cvCalcGlobalOrientation(const CvArr* orientation, const CvArr* mask, const CvArr* mhi, double timestamp, double duration)

lib/Cv/JA.pod  view on Meta::CPAN

OpenCV のシーケンスは、点、矩形、円など様々なデータを格納します。格納さ
れたデータを型なしで扱うスーパクラス L<Cv::Seq> と、データに合わせた変
換を行う派生クラス C<Cv::Seq::Point>, C<Cv::Seq::Rect>, ... に分け、デー
タに合わせて blessして使います。C言語のキャストのようなものです。

facedetect の一部を示します。HaarDetectObjects() は、検出した顔を複数の
CvRect 型のデータのリストにして返します。次の例は、シーケンスを
C<Cv::Seq::Rect> で bless して、顔を 1つずつ取り出します。

  my $faces = bless $image->HaarDetectObjects(
    $cascade, $storage, 1.1, 2, CV_HAAR_SCALE_IMAGE,
    cvSize(30, 30)), "Cv::Seq::Rect";
  while (my @rect = $faces->shift) {
    my ($x, $y, $w, $h) = @rect;
    ...
  }

同じ部分の C言語のコードを並べておきます。比べやすくするために、余分な
ものは省きました。

  CvSeq* faces = cvHaarDetectObjects(
    image, cascade, storage, 1.1, 2, CV_HAAR_SCALE_IMAGE,
    cvSize(30, 30));
  for (;;) {
    CvRect rect;
    cvSeqPopFront(faces, &rect);
    ...
  }

=head2 メソッド

=over

sample/facedetect.pl  view on Meta::CPAN

#!/usr/bin/perl
# -*- mode: perl; coding: utf-8; tab-width: 4; -*-

use strict;
use warnings;
use lib qw(blib/lib blib/arch);
use Cv;

use File::Basename;

my $haarDir = "/usr/local/share/OpenCV/haarcascades";

sub help {
	die << "----";

This program demonstrates the haar cascade recognizer this classifier
can recognize many ~rigid objects, it\'s most known use is for faces.

Usage:
\$ .\/facedetect
	[--cascade=<cascade_path>]
		this is the primary trained classifier such as frontal face
	[--nested-cascade[=<nested_cascade_path>]]
		this an optional secondary classifier such as eyes
	[--scale=<image scale greater or equal to 1, try 1.3 for example>]
	[filename|camera_index]

see facedetect.cmd for one call:
\$ .\/facedetect
	--cascade=$haarDir/haarcascade_frontalface_alt.xml
	--nested-cascade=$haarDir/haarcascade_eye.xml
	--scale=1.3

Hit any key to quit.
----
	 ;
}

my $cascadeName       = "$haarDir/haarcascade_frontalface_alt.xml";
my $nestedCascadeName = "$haarDir/haarcascade_eye_tree_eyeglasses.xml";
my $scale = 1;

use Getopt::Long;
unless (GetOptions(
	"--cascade=s" => \$cascadeName,
	"--nested-cascade=s" => \$nestedCascadeName,
	"--scale=s" => \$scale,
		)) {
	help();
}

my $inputName = shift(@ARGV);
my $cascade = Cv->Load($cascadeName);
unless ($cascade) {
	die "ERROR: Could not load classifier cascade";
}
my $nestedCascade = Cv->Load($nestedCascadeName);
unless ($nestedCascade) {
	warn "WARNING: Could not load classifier cascade for nested objects\n";
}

my $capture;
my $image;
if (defined $inputName && $inputName =~ /^\d$/) {
    $capture = Cv->captureFromCAM($inputName);
	unless ($capture) {
		warn "Capture from CAM $inputName didn't work\n";
	}
} elsif ($inputName) {

sample/facedetect.pl  view on Meta::CPAN

unless ($capture || $image) {
	my $lena = dirname($0) . "/lena.jpg";
	$image = Cv->loadImage($lena);
}

Cv->NamedWindow("result", 0);

if ($capture) {
	while (my $frame = $capture->queryFrame) {
		my $frameCopy = $frame->flip(\0, 1)->clone;
		$frameCopy->detectAndDraw($cascade, $nestedCascade, $scale);
		last if Cv->waitKey(10) > 0;
	}
} elsif ($image) {
	$image->detectAndDraw($cascade, $nestedCascade, $scale);
	Cv->waitKey;
}

sub Cv::Arr::detectAndDraw {
	my ($img, $cascade, $nestedCascade, $scale) = @_;
    my @colors = (
		CV_RGB(  0,   0, 255),
        CV_RGB(  0, 128, 255),
        CV_RGB(  0, 255, 255),
        CV_RGB(  0, 255,   0),
        CV_RGB(255, 128,   0),
        CV_RGB(255, 255,   0),
        CV_RGB(255,   0,   0),
        CV_RGB(255,   0, 255)
		);

sample/facedetect.pl  view on Meta::CPAN

	{ package Cv::Seq::FaceDetect;
	  our @ISA = qw(Cv::Seq::Point);
	  sub template {
		  my $self = CORE::shift;
		  my ($t, $c) = ("i5", 5);
		  wantarray? ($t, $c) : $t;
	  }
	}
	my $faces = bless $smallImg->HaarDetectObjects(
		$cascade, $storage, 1.1, 2, 0
        # |CV_HAAR_FIND_BIGGEST_OBJECT
        # |CV_HAAR_DO_ROUGH_SEARCH
        |CV_HAAR_SCALE_IMAGE
		,
        cvSize(30, 30)
		), "Cv::Seq::FaceDetect";
    $t = Cv->getTickCount() - $t;
    printf("detection time = %g ms\n", $t/(Cv->getTickFrequency()*1000));
	my $i = 0;
	for my $face (@$faces) {