976 lines
27 KiB
ReStructuredText
976 lines
27 KiB
ReStructuredText
Feature detection and description
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=================================
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.. highlight:: cpp
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.. index:: FAST
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cv::FAST
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--------
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`id=0.180338558353 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/FAST>`__
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.. cfunction:: void FAST( const Mat\& image, vector<KeyPoint>\& keypoints, int threshold, bool nonmaxSupression=true )
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Detects corners using FAST algorithm by E. Rosten (''Machine learning for high-speed corner detection'', 2006).
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:param image: The image. Keypoints (corners) will be detected on this.
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:param keypoints: Keypoints detected on the image.
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:param threshold: Threshold on difference between intensity of center pixel and
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pixels on circle around this pixel. See description of the algorithm.
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:param nonmaxSupression: If it is true then non-maximum supression will be applied to detected corners (keypoints).
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.. index:: MSER
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.. _MSER:
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MSER
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----
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`id=0.0333368188128 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/MSER>`__
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.. ctype:: MSER
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Maximally-Stable Extremal Region Extractor
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class MSER : public CvMSERParams
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{
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public:
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// default constructor
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MSER();
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// constructor that initializes all the algorithm parameters
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MSER( int _delta, int _min_area, int _max_area,
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float _max_variation, float _min_diversity,
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int _max_evolution, double _area_threshold,
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double _min_margin, int _edge_blur_size );
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// runs the extractor on the specified image; returns the MSERs,
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// each encoded as a contour (vector<Point>, see findContours)
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// the optional mask marks the area where MSERs are searched for
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void operator()( const Mat& image, vector<vector<Point> >& msers, const Mat& mask ) const;
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};
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..
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The class encapsulates all the parameters of MSER (see
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http://en.wikipedia.org/wiki/Maximally_stable_extremal_regions
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) extraction algorithm.
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.. index:: StarDetector
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.. _StarDetector:
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StarDetector
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------------
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`id=0.378812518152 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/StarDetector>`__
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.. ctype:: StarDetector
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Implements Star keypoint detector
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class StarDetector : CvStarDetectorParams
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{
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public:
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// default constructor
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StarDetector();
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// the full constructor initialized all the algorithm parameters:
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// maxSize - maximum size of the features. The following
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// values of the parameter are supported:
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// 4, 6, 8, 11, 12, 16, 22, 23, 32, 45, 46, 64, 90, 128
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// responseThreshold - threshold for the approximated laplacian,
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// used to eliminate weak features. The larger it is,
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// the less features will be retrieved
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// lineThresholdProjected - another threshold for the laplacian to
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// eliminate edges
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// lineThresholdBinarized - another threshold for the feature
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// size to eliminate edges.
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// The larger the 2 threshold, the more points you get.
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StarDetector(int maxSize, int responseThreshold,
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int lineThresholdProjected,
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int lineThresholdBinarized,
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int suppressNonmaxSize);
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// finds keypoints in an image
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void operator()(const Mat& image, vector<KeyPoint>& keypoints) const;
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};
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..
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The class implements a modified version of CenSurE keypoint detector described in
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Agrawal08
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.. index:: SIFT
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.. _SIFT:
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SIFT
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----
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`id=0.385373212311 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/SIFT>`__
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.. ctype:: SIFT
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Class for extracting keypoints and computing descriptors using approach named Scale Invariant Feature Transform (SIFT).
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::
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class CV_EXPORTS SIFT
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{
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public:
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struct CommonParams
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{
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static const int DEFAULT_NOCTAVES = 4;
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static const int DEFAULT_NOCTAVE_LAYERS = 3;
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static const int DEFAULT_FIRST_OCTAVE = -1;
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enum{ FIRST_ANGLE = 0, AVERAGE_ANGLE = 1 };
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CommonParams();
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CommonParams( int _nOctaves, int _nOctaveLayers, int _firstOctave,
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int _angleMode );
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int nOctaves, nOctaveLayers, firstOctave;
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int angleMode;
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};
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struct DetectorParams
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{
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static double GET_DEFAULT_THRESHOLD()
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{ return 0.04 / SIFT::CommonParams::DEFAULT_NOCTAVE_LAYERS / 2.0; }
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static double GET_DEFAULT_EDGE_THRESHOLD() { return 10.0; }
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DetectorParams();
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DetectorParams( double _threshold, double _edgeThreshold );
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double threshold, edgeThreshold;
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};
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struct DescriptorParams
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{
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static double GET_DEFAULT_MAGNIFICATION() { return 3.0; }
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static const bool DEFAULT_IS_NORMALIZE = true;
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static const int DESCRIPTOR_SIZE = 128;
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DescriptorParams();
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DescriptorParams( double _magnification, bool _isNormalize,
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bool _recalculateAngles );
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double magnification;
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bool isNormalize;
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bool recalculateAngles;
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};
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SIFT();
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//! sift-detector constructor
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SIFT( double _threshold, double _edgeThreshold,
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int _nOctaves=CommonParams::DEFAULT_NOCTAVES,
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int _nOctaveLayers=CommonParams::DEFAULT_NOCTAVE_LAYERS,
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int _firstOctave=CommonParams::DEFAULT_FIRST_OCTAVE,
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int _angleMode=CommonParams::FIRST_ANGLE );
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//! sift-descriptor constructor
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SIFT( double _magnification, bool _isNormalize=true,
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bool _recalculateAngles = true,
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int _nOctaves=CommonParams::DEFAULT_NOCTAVES,
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int _nOctaveLayers=CommonParams::DEFAULT_NOCTAVE_LAYERS,
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int _firstOctave=CommonParams::DEFAULT_FIRST_OCTAVE,
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int _angleMode=CommonParams::FIRST_ANGLE );
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SIFT( const CommonParams& _commParams,
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const DetectorParams& _detectorParams = DetectorParams(),
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const DescriptorParams& _descriptorParams = DescriptorParams() );
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//! returns the descriptor size in floats (128)
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int descriptorSize() const { return DescriptorParams::DESCRIPTOR_SIZE; }
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//! finds the keypoints using SIFT algorithm
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void operator()(const Mat& img, const Mat& mask,
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vector<KeyPoint>& keypoints) const;
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//! finds the keypoints and computes descriptors for them using SIFT algorithm.
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//! Optionally it can compute descriptors for the user-provided keypoints
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void operator()(const Mat& img, const Mat& mask,
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vector<KeyPoint>& keypoints,
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Mat& descriptors,
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bool useProvidedKeypoints=false) const;
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CommonParams getCommonParams () const { return commParams; }
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DetectorParams getDetectorParams () const { return detectorParams; }
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DescriptorParams getDescriptorParams () const { return descriptorParams; }
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protected:
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...
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};
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..
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.. index:: SURF
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.. _SURF:
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SURF
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----
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`id=0.43149154692 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/SURF>`__
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.. ctype:: SURF
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Class for extracting Speeded Up Robust Features from an image.
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class SURF : public CvSURFParams
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{
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public:
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// default constructor
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SURF();
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// constructor that initializes all the algorithm parameters
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SURF(double _hessianThreshold, int _nOctaves=4,
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int _nOctaveLayers=2, bool _extended=false);
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// returns the number of elements in each descriptor (64 or 128)
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int descriptorSize() const;
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// detects keypoints using fast multi-scale Hessian detector
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void operator()(const Mat& img, const Mat& mask,
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vector<KeyPoint>& keypoints) const;
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// detects keypoints and computes the SURF descriptors for them;
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// output vector "descriptors" stores elements of descriptors and has size
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// equal descriptorSize()*keypoints.size() as each descriptor is
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// descriptorSize() elements of this vector.
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void operator()(const Mat& img, const Mat& mask,
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vector<KeyPoint>& keypoints,
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vector<float>& descriptors,
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bool useProvidedKeypoints=false) const;
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};
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..
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The class
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``SURF``
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implements Speeded Up Robust Features descriptor
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Bay06
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There is fast multi-scale Hessian keypoint detector that can be used to find the keypoints
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(which is the default option), but the descriptors can be also computed for the user-specified keypoints.
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The function can be used for object tracking and localization, image stitching etc. See the
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``find_obj.cpp``
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demo in OpenCV samples directory.
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.. index:: RandomizedTree
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.. _RandomizedTree:
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RandomizedTree
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--------------
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`id=0.539311466248 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/RandomizedTree>`__
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.. ctype:: RandomizedTree
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The class contains base structure for
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``RTreeClassifier``
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class CV_EXPORTS RandomizedTree
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{
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public:
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friend class RTreeClassifier;
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RandomizedTree();
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~RandomizedTree();
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void train(std::vector<BaseKeypoint> const& base_set,
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cv::RNG &rng, int depth, int views,
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size_t reduced_num_dim, int num_quant_bits);
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void train(std::vector<BaseKeypoint> const& base_set,
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cv::RNG &rng, PatchGenerator &make_patch, int depth,
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int views, size_t reduced_num_dim, int num_quant_bits);
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// following two funcs are EXPERIMENTAL
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//(do not use unless you know exactly what you do)
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static void quantizeVector(float *vec, int dim, int N, float bnds[2],
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int clamp_mode=0);
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static void quantizeVector(float *src, int dim, int N, float bnds[2],
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uchar *dst);
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// patch_data must be a 32x32 array (no row padding)
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float* getPosterior(uchar* patch_data);
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const float* getPosterior(uchar* patch_data) const;
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uchar* getPosterior2(uchar* patch_data);
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void read(const char* file_name, int num_quant_bits);
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void read(std::istream &is, int num_quant_bits);
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void write(const char* file_name) const;
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void write(std::ostream &os) const;
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int classes() { return classes_; }
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int depth() { return depth_; }
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void discardFloatPosteriors() { freePosteriors(1); }
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inline void applyQuantization(int num_quant_bits)
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{ makePosteriors2(num_quant_bits); }
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private:
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int classes_;
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int depth_;
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int num_leaves_;
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std::vector<RTreeNode> nodes_;
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float **posteriors_; // 16-bytes aligned posteriors
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uchar **posteriors2_; // 16-bytes aligned posteriors
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std::vector<int> leaf_counts_;
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void createNodes(int num_nodes, cv::RNG &rng);
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void allocPosteriorsAligned(int num_leaves, int num_classes);
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void freePosteriors(int which);
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// which: 1=posteriors_, 2=posteriors2_, 3=both
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void init(int classes, int depth, cv::RNG &rng);
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void addExample(int class_id, uchar* patch_data);
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void finalize(size_t reduced_num_dim, int num_quant_bits);
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int getIndex(uchar* patch_data) const;
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inline float* getPosteriorByIndex(int index);
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inline uchar* getPosteriorByIndex2(int index);
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inline const float* getPosteriorByIndex(int index) const;
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void convertPosteriorsToChar();
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void makePosteriors2(int num_quant_bits);
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void compressLeaves(size_t reduced_num_dim);
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void estimateQuantPercForPosteriors(float perc[2]);
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};
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..
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.. index:: RandomizedTree::train
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cv::RandomizedTree::train
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-------------------------
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`id=0.360469298211 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/RandomizedTree%3A%3Atrain>`__
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.. cfunction:: void train(std::vector<BaseKeypoint> const\& base_set, cv::RNG \&rng, PatchGenerator \&make_patch, int depth, int views, size_t reduced_num_dim, int num_quant_bits)
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Trains a randomized tree using input set of keypoints
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.. cfunction:: void train(std::vector<BaseKeypoint> const\& base_set, cv::RNG \&rng, PatchGenerator \&make_patch, int depth, int views, size_t reduced_num_dim, int num_quant_bits)
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{Vector of
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``BaseKeypoint``
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type. Contains keypoints from the image are used for training}
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{Random numbers generator is used for training}
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{Patch generator is used for training}
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{Maximum tree depth}
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{Number of dimensions are used in compressed signature}
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{Number of bits are used for quantization}
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.. index:: RandomizedTree::read
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cv::RandomizedTree::read
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------------------------
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`id=0.663893576705 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/RandomizedTree%3A%3Aread>`__
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.. cfunction:: read(const char* file_name, int num_quant_bits)
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Reads pre-saved randomized tree from file or stream
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.. cfunction:: read(std::istream \&is, int num_quant_bits)
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:param file_name: Filename of file contains randomized tree data
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:param is: Input stream associated with file contains randomized tree data
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{Number of bits are used for quantization}
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.. index:: RandomizedTree::write
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cv::RandomizedTree::write
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-------------------------
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`id=0.640726433619 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/RandomizedTree%3A%3Awrite>`__
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.. cfunction:: void write(const char* file_name) const
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Writes current randomized tree to a file or stream
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.. cfunction:: void write(std::ostream \&os) const
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:param file_name: Filename of file where randomized tree data will be stored
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:param is: Output stream associated with file where randomized tree data will be stored
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.. index:: RandomizedTree::applyQuantization
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cv::RandomizedTree::applyQuantization
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-------------------------------------
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`id=0.113364904421 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/RandomizedTree%3A%3AapplyQuantization>`__
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.. cfunction:: void applyQuantization(int num_quant_bits)
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Applies quantization to the current randomized tree
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{Number of bits are used for quantization}
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.. index:: RTreeNode
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.. _RTreeNode:
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RTreeNode
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---------
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`id=0.718763052087 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/RTreeNode>`__
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.. ctype:: RTreeNode
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The class contains base structure for
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``RandomizedTree``
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struct RTreeNode
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{
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short offset1, offset2;
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RTreeNode() {}
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RTreeNode(uchar x1, uchar y1, uchar x2, uchar y2)
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: offset1(y1*PATCH_SIZE + x1),
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offset2(y2*PATCH_SIZE + x2)
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{}
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//! Left child on 0, right child on 1
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inline bool operator() (uchar* patch_data) const
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{
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return patch_data[offset1] > patch_data[offset2];
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}
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};
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..
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.. index:: RTreeClassifier
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.. _RTreeClassifier:
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RTreeClassifier
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---------------
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`id=0.477872539921 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/RTreeClassifier>`__
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.. ctype:: RTreeClassifier
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The class contains
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``RTreeClassifier``
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. It represents calonder descriptor which was originally introduced by Michael Calonder
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::
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class CV_EXPORTS RTreeClassifier
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{
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public:
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static const int DEFAULT_TREES = 48;
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static const size_t DEFAULT_NUM_QUANT_BITS = 4;
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RTreeClassifier();
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void train(std::vector<BaseKeypoint> const& base_set,
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cv::RNG &rng,
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int num_trees = RTreeClassifier::DEFAULT_TREES,
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int depth = DEFAULT_DEPTH,
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int views = DEFAULT_VIEWS,
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size_t reduced_num_dim = DEFAULT_REDUCED_NUM_DIM,
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int num_quant_bits = DEFAULT_NUM_QUANT_BITS,
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bool print_status = true);
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void train(std::vector<BaseKeypoint> const& base_set,
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cv::RNG &rng,
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PatchGenerator &make_patch,
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int num_trees = RTreeClassifier::DEFAULT_TREES,
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int depth = DEFAULT_DEPTH,
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int views = DEFAULT_VIEWS,
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size_t reduced_num_dim = DEFAULT_REDUCED_NUM_DIM,
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int num_quant_bits = DEFAULT_NUM_QUANT_BITS,
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bool print_status = true);
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// sig must point to a memory block of at least
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//classes()*sizeof(float|uchar) bytes
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void getSignature(IplImage *patch, uchar *sig);
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void getSignature(IplImage *patch, float *sig);
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void getSparseSignature(IplImage *patch, float *sig,
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float thresh);
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static int countNonZeroElements(float *vec, int n, double tol=1e-10);
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static inline void safeSignatureAlloc(uchar **sig, int num_sig=1,
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int sig_len=176);
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static inline uchar* safeSignatureAlloc(int num_sig=1,
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int sig_len=176);
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inline int classes() { return classes_; }
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inline int original_num_classes()
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{ return original_num_classes_; }
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void setQuantization(int num_quant_bits);
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void discardFloatPosteriors();
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void read(const char* file_name);
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void read(std::istream &is);
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void write(const char* file_name) const;
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void write(std::ostream &os) const;
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std::vector<RandomizedTree> trees_;
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private:
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int classes_;
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int num_quant_bits_;
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uchar **posteriors_;
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ushort *ptemp_;
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int original_num_classes_;
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bool keep_floats_;
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};
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..
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.. index:: RTreeClassifier::train
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cv::RTreeClassifier::train
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--------------------------
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`id=0.173927228061 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/RTreeClassifier%3A%3Atrain>`__
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.. cfunction:: void train(std::vector<BaseKeypoint> const\& base_set, cv::RNG \&rng, int num_trees = RTreeClassifier::DEFAULT_TREES, int depth = DEFAULT_DEPTH, int views = DEFAULT_VIEWS, size_t reduced_num_dim = DEFAULT_REDUCED_NUM_DIM, int num_quant_bits = DEFAULT_NUM_QUANT_BITS, bool print_status = true)
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Trains a randomized tree classificator using input set of keypoints
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.. cfunction:: void train(std::vector<BaseKeypoint> const\& base_set, cv::RNG \&rng, PatchGenerator \&make_patch, int num_trees = RTreeClassifier::DEFAULT_TREES, int depth = DEFAULT_DEPTH, int views = DEFAULT_VIEWS, size_t reduced_num_dim = DEFAULT_REDUCED_NUM_DIM, int num_quant_bits = DEFAULT_NUM_QUANT_BITS, bool print_status = true)
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{Vector of
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``BaseKeypoint``
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type. Contains keypoints from the image are used for training}
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{Random numbers generator is used for training}
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{Patch generator is used for training}
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{Number of randomized trees used in RTreeClassificator}
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{Maximum tree depth}
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{Number of dimensions are used in compressed signature}
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{Number of bits are used for quantization}
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{Print current status of training on the console}
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.. index:: RTreeClassifier::getSignature
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cv::RTreeClassifier::getSignature
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---------------------------------
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`id=0.90043980708 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/RTreeClassifier%3A%3AgetSignature>`__
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.. cfunction:: void getSignature(IplImage *patch, uchar *sig)
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Returns signature for image patch
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.. cfunction:: void getSignature(IplImage *patch, float *sig)
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{Image patch to calculate signature for}
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{Output signature (array dimension is
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``reduced_num_dim)``
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}
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.. index:: RTreeClassifier::getSparseSignature
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cv::RTreeClassifier::getSparseSignature
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---------------------------------------
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|
`id=0.692099737961 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/RTreeClassifier%3A%3AgetSparseSignature>`__
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````
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.. cfunction:: void getSparseSignature(IplImage *patch, float *sig, float thresh)
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The function is simular to getSignaturebut uses the threshold for removing all signature elements less than the threshold. So that the signature is compressed
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{Image patch to calculate signature for}
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{Output signature (array dimension is
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``reduced_num_dim)``
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}
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{The threshold that is used for compressing the signature}
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.. index:: RTreeClassifier::countNonZeroElements
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cv::RTreeClassifier::countNonZeroElements
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-----------------------------------------
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|
`id=0.553226961988 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/RTreeClassifier%3A%3AcountNonZeroElements>`__
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.. cfunction:: static int countNonZeroElements(float *vec, int n, double tol=1e-10)
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|
The function returns the number of non-zero elements in the input array.
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:param vec: Input vector contains float elements
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:param n: Input vector size
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{The threshold used for elements counting. We take all elements are less than
|
|
``tol``
|
|
as zero elements}
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|
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.. index:: RTreeClassifier::read
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|
|
|
|
|
cv::RTreeClassifier::read
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|
-------------------------
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|
`id=0.648907224792 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/RTreeClassifier%3A%3Aread>`__
|
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|
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.. cfunction:: read(const char* file_name)
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|
|
Reads pre-saved RTreeClassifier from file or stream
|
|
|
|
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|
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|
.. cfunction:: read(std::istream \&is)
|
|
|
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:param file_name: Filename of file contains randomized tree data
|
|
|
|
|
|
:param is: Input stream associated with file contains randomized tree data
|
|
|
|
|
|
|
|
|
|
.. index:: RTreeClassifier::write
|
|
|
|
|
|
cv::RTreeClassifier::write
|
|
--------------------------
|
|
|
|
`id=0.340545032412 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/RTreeClassifier%3A%3Awrite>`__
|
|
|
|
|
|
|
|
|
|
.. cfunction:: void write(const char* file_name) const
|
|
|
|
Writes current RTreeClassifier to a file or stream
|
|
|
|
|
|
|
|
|
|
.. cfunction:: void write(std::ostream \&os) const
|
|
|
|
|
|
|
|
|
|
|
|
|
|
:param file_name: Filename of file where randomized tree data will be stored
|
|
|
|
|
|
:param is: Output stream associated with file where randomized tree data will be stored
|
|
|
|
|
|
|
|
|
|
.. index:: RTreeClassifier::setQuantization
|
|
|
|
|
|
cv::RTreeClassifier::setQuantization
|
|
------------------------------------
|
|
|
|
`id=0.788175788924 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/features2d/RTreeClassifier%3A%3AsetQuantization>`__
|
|
|
|
|
|
|
|
|
|
.. cfunction:: void setQuantization(int num_quant_bits)
|
|
|
|
Applies quantization to the current randomized tree
|
|
|
|
|
|
|
|
|
|
{Number of bits are used for quantization}
|
|
|
|
|
|
Below there is an example of
|
|
``RTreeClassifier``
|
|
usage for feature matching. There are test and train images and we extract features from both with SURF. Output is
|
|
:math:`best\_corr`
|
|
and
|
|
:math:`best\_corr\_idx`
|
|
arrays which keep the best probabilities and corresponding features indexes for every train feature.
|
|
|
|
|
|
|
|
|
|
::
|
|
|
|
|
|
|
|
CvMemStorage* storage = cvCreateMemStorage(0);
|
|
CvSeq *objectKeypoints = 0, *objectDescriptors = 0;
|
|
CvSeq *imageKeypoints = 0, *imageDescriptors = 0;
|
|
CvSURFParams params = cvSURFParams(500, 1);
|
|
cvExtractSURF( test_image, 0, &imageKeypoints, &imageDescriptors,
|
|
storage, params );
|
|
cvExtractSURF( train_image, 0, &objectKeypoints, &objectDescriptors,
|
|
storage, params );
|
|
|
|
cv::RTreeClassifier detector;
|
|
int patch_width = cv::PATCH_SIZE;
|
|
iint patch_height = cv::PATCH_SIZE;
|
|
vector<cv::BaseKeypoint> base_set;
|
|
int i=0;
|
|
CvSURFPoint* point;
|
|
for (i=0;i<(n_points > 0 ? n_points : objectKeypoints->total);i++)
|
|
{
|
|
point=(CvSURFPoint*)cvGetSeqElem(objectKeypoints,i);
|
|
base_set.push_back(
|
|
cv::BaseKeypoint(point->pt.x,point->pt.y,train_image));
|
|
}
|
|
|
|
//Detector training
|
|
cv::RNG rng( cvGetTickCount() );
|
|
cv::PatchGenerator gen(0,255,2,false,0.7,1.3,-CV_PI/3,CV_PI/3,
|
|
-CV_PI/3,CV_PI/3);
|
|
|
|
printf("RTree Classifier training...n");
|
|
detector.train(base_set,rng,gen,24,cv::DEFAULT_DEPTH,2000,
|
|
(int)base_set.size(), detector.DEFAULT_NUM_QUANT_BITS);
|
|
printf("Donen");
|
|
|
|
float* signature = new float[detector.original_num_classes()];
|
|
float* best_corr;
|
|
int* best_corr_idx;
|
|
if (imageKeypoints->total > 0)
|
|
{
|
|
best_corr = new float[imageKeypoints->total];
|
|
best_corr_idx = new int[imageKeypoints->total];
|
|
}
|
|
|
|
for(i=0; i < imageKeypoints->total; i++)
|
|
{
|
|
point=(CvSURFPoint*)cvGetSeqElem(imageKeypoints,i);
|
|
int part_idx = -1;
|
|
float prob = 0.0f;
|
|
|
|
CvRect roi = cvRect((int)(point->pt.x) - patch_width/2,
|
|
(int)(point->pt.y) - patch_height/2,
|
|
patch_width, patch_height);
|
|
cvSetImageROI(test_image, roi);
|
|
roi = cvGetImageROI(test_image);
|
|
if(roi.width != patch_width || roi.height != patch_height)
|
|
{
|
|
best_corr_idx[i] = part_idx;
|
|
best_corr[i] = prob;
|
|
}
|
|
else
|
|
{
|
|
cvSetImageROI(test_image, roi);
|
|
IplImage* roi_image =
|
|
cvCreateImage(cvSize(roi.width, roi.height),
|
|
test_image->depth, test_image->nChannels);
|
|
cvCopy(test_image,roi_image);
|
|
|
|
detector.getSignature(roi_image, signature);
|
|
for (int j = 0; j< detector.original_num_classes();j++)
|
|
{
|
|
if (prob < signature[j])
|
|
{
|
|
part_idx = j;
|
|
prob = signature[j];
|
|
}
|
|
}
|
|
|
|
best_corr_idx[i] = part_idx;
|
|
best_corr[i] = prob;
|
|
|
|
|
|
if (roi_image)
|
|
cvReleaseImage(&roi_image);
|
|
}
|
|
cvResetImageROI(test_image);
|
|
}
|
|
|
|
|
|
|
|
..
|
|
|