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#ifndef _CVTYPES_H_
#define _CVTYPES_H_
#ifndef SKIP_INCLUDES
#include <assert.h>
#include <stdlib.h>
#endif
/* spatial and central moments */
typedef struct CvMoments
{
double m00, m10, m01, m20, m11, m02, m30, m21, m12, m03; /* spatial moments */
double mu20, mu11, mu02, mu30, mu21, mu12, mu03; /* central moments */
double inv_sqrt_m00; /* m00 != 0 ? 1/sqrt(m00) : 0 */
}
CvMoments;
/* Hu invariants */
typedef struct CvHuMoments
{
double hu1, hu2, hu3, hu4, hu5, hu6, hu7; /* Hu invariants */
}
CvHuMoments;
/**************************** Connected Component **************************************/
typedef struct CvConnectedComp
{
double area; /* area of the connected component */
CvScalar value; /* average color of the connected component */
CvRect rect; /* ROI of the component */
CvSeq* contour; /* optional component boundary
(the contour might have child contours corresponding to the holes)*/
}
CvConnectedComp;
/*
Internal structure that is used for sequental retrieving contours from the image.
It supports both hierarchical and plane variants of Suzuki algorithm.
*/
typedef struct _CvContourScanner* CvContourScanner;
/* contour retrieval mode */
#define CV_RETR_EXTERNAL 0
#define CV_RETR_LIST 1
#define CV_RETR_CCOMP 2
#define CV_RETR_TREE 3
/* contour approximation method */
#define CV_CHAIN_CODE 0
#define CV_CHAIN_APPROX_NONE 1
#define CV_CHAIN_APPROX_SIMPLE 2
#define CV_CHAIN_APPROX_TC89_L1 3
#define CV_CHAIN_APPROX_TC89_KCOS 4
#define CV_LINK_RUNS 5
/* Freeman chain reader state */
typedef struct CvChainPtReader
{
CV_SEQ_READER_FIELDS()
char code;
CvPoint pt;
schar deltas[8][2];
}
CvChainPtReader;
/* initializes 8-element array for fast access to 3x3 neighborhood of a pixel */
#define CV_INIT_3X3_DELTAS( deltas, step, nch ) \
((deltas)[0] = (nch), (deltas)[1] = -(step) + (nch), \
(deltas)[2] = -(step), (deltas)[3] = -(step) - (nch), \
(deltas)[4] = -(nch), (deltas)[5] = (step) - (nch), \
(deltas)[6] = (step), (deltas)[7] = (step) + (nch))
/* Contour tree header */
typedef struct CvContourTree
{
CV_SEQUENCE_FIELDS()
CvPoint p1; /* the first point of the binary tree root segment */
CvPoint p2; /* the last point of the binary tree root segment */
}
CvContourTree;
/* Finds a sequence of convexity defects of given contour */
typedef struct CvConvexityDefect
{
CvPoint* start; /* point of the contour where the defect begins */
CvPoint* end; /* point of the contour where the defect ends */
CvPoint* depth_point; /* the farthest from the convex hull point within the defect */
float depth; /* distance between the farthest point and the convex hull */
}
CvConvexityDefect;
/************ Data structures and related enumerations for Planar Subdivisions ************/
typedef size_t CvSubdiv2DEdge;
#define CV_QUADEDGE2D_FIELDS() \
int flags; \
struct CvSubdiv2DPoint* pt[4]; \
CvSubdiv2DEdge next[4];
#define CV_SUBDIV2D_POINT_FIELDS()\
int flags; \
CvSubdiv2DEdge first; \
CvPoint2D32f pt;
#define CV_SUBDIV2D_VIRTUAL_POINT_FLAG (1 << 30)
typedef struct CvQuadEdge2D
{
CV_QUADEDGE2D_FIELDS()
}
CvQuadEdge2D;
typedef struct CvSubdiv2DPoint
{
CV_SUBDIV2D_POINT_FIELDS()
}
CvSubdiv2DPoint;
#define CV_SUBDIV2D_FIELDS() \
CV_GRAPH_FIELDS() \
int quad_edges; \
int is_geometry_valid; \
CvSubdiv2DEdge recent_edge; \
CvPoint2D32f topleft; \
CvPoint2D32f bottomright;
typedef struct CvSubdiv2D
{
CV_SUBDIV2D_FIELDS()
}
CvSubdiv2D;
typedef enum CvSubdiv2DPointLocation
{
CV_PTLOC_ERROR = -2,
CV_PTLOC_OUTSIDE_RECT = -1,
CV_PTLOC_INSIDE = 0,
CV_PTLOC_VERTEX = 1,
CV_PTLOC_ON_EDGE = 2
}
CvSubdiv2DPointLocation;
typedef enum CvNextEdgeType
{
CV_NEXT_AROUND_ORG = 0x00,
CV_NEXT_AROUND_DST = 0x22,
CV_PREV_AROUND_ORG = 0x11,
CV_PREV_AROUND_DST = 0x33,
CV_NEXT_AROUND_LEFT = 0x13,
CV_NEXT_AROUND_RIGHT = 0x31,
CV_PREV_AROUND_LEFT = 0x20,
CV_PREV_AROUND_RIGHT = 0x02
}
CvNextEdgeType;
/* get the next edge with the same origin point (counterwise) */
#define CV_SUBDIV2D_NEXT_EDGE( edge ) (((CvQuadEdge2D*)((edge) & ~3))->next[(edge)&3])
/* Defines for Distance Transform */
#define CV_DIST_USER -1 /* User defined distance */
#define CV_DIST_L1 1 /* distance = |x1-x2| + |y1-y2| */
#define CV_DIST_L2 2 /* the simple euclidean distance */
#define CV_DIST_C 3 /* distance = max(|x1-x2|,|y1-y2|) */
#define CV_DIST_L12 4 /* L1-L2 metric: distance = 2(sqrt(1+x*x/2) - 1)) */
#define CV_DIST_FAIR 5 /* distance = c^2(|x|/c-log(1+|x|/c)), c = 1.3998 */
#define CV_DIST_WELSCH 6 /* distance = c^2/2(1-exp(-(x/c)^2)), c = 2.9846 */
#define CV_DIST_HUBER 7 /* distance = |x|<c ? x^2/2 : c(|x|-c/2), c=1.345 */
/* Filters used in pyramid decomposition */
typedef enum CvFilter
{
CV_GAUSSIAN_5x5 = 7
}
CvFilter;
/****************************************************************************************/
/* Older definitions */
/****************************************************************************************/
typedef float* CvVect32f;
typedef float* CvMatr32f;
typedef double* CvVect64d;
typedef double* CvMatr64d;
typedef struct CvMatrix3
{
float m[3][3];
}
CvMatrix3;
#ifdef __cplusplus
extern "C" {
#endif
typedef float (CV_CDECL * CvDistanceFunction)( const float* a, const float* b, void* user_param );
#ifdef __cplusplus
}
#endif
typedef struct CvConDensation
{
int MP;
int DP;
float* DynamMatr; /* Matrix of the linear Dynamics system */
float* State; /* Vector of State */
int SamplesNum; /* Number of the Samples */
float** flSamples; /* arr of the Sample Vectors */
float** flNewSamples; /* temporary array of the Sample Vectors */
float* flConfidence; /* Confidence for each Sample */
float* flCumulative; /* Cumulative confidence */
float* Temp; /* Temporary vector */
float* RandomSample; /* RandomVector to update sample set */
struct CvRandState* RandS; /* Array of structures to generate random vectors */
}
CvConDensation;
/*
standard Kalman filter (in G. Welch' and G. Bishop's notation):
x(k)=A*x(k-1)+B*u(k)+w(k) p(w)~N(0,Q)
z(k)=H*x(k)+v(k), p(v)~N(0,R)
*/
typedef struct CvKalman
{
int MP; /* number of measurement vector dimensions */
int DP; /* number of state vector dimensions */
int CP; /* number of control vector dimensions */
/* backward compatibility fields */
#if 1
float* PosterState; /* =state_pre->data.fl */
float* PriorState; /* =state_post->data.fl */
float* DynamMatr; /* =transition_matrix->data.fl */
float* MeasurementMatr; /* =measurement_matrix->data.fl */
float* MNCovariance; /* =measurement_noise_cov->data.fl */
float* PNCovariance; /* =process_noise_cov->data.fl */
float* KalmGainMatr; /* =gain->data.fl */
float* PriorErrorCovariance;/* =error_cov_pre->data.fl */
float* PosterErrorCovariance;/* =error_cov_post->data.fl */
float* Temp1; /* temp1->data.fl */
float* Temp2; /* temp2->data.fl */
#endif
CvMat* state_pre; /* predicted state (x'(k)):
x(k)=A*x(k-1)+B*u(k) */
CvMat* state_post; /* corrected state (x(k)):
x(k)=x'(k)+K(k)*(z(k)-H*x'(k)) */
CvMat* transition_matrix; /* state transition matrix (A) */
CvMat* control_matrix; /* control matrix (B)
(it is not used if there is no control)*/
CvMat* measurement_matrix; /* measurement matrix (H) */
CvMat* process_noise_cov; /* process noise covariance matrix (Q) */
CvMat* measurement_noise_cov; /* measurement noise covariance matrix (R) */
CvMat* error_cov_pre; /* priori error estimate covariance matrix (P'(k)):
P'(k)=A*P(k-1)*At + Q)*/
CvMat* gain; /* Kalman gain matrix (K(k)):
K(k)=P'(k)*Ht*inv(H*P'(k)*Ht+R)*/
CvMat* error_cov_post; /* posteriori error estimate covariance matrix (P(k)):
P(k)=(I-K(k)*H)*P'(k) */
CvMat* temp1; /* temporary matrices */
CvMat* temp2;
CvMat* temp3;
CvMat* temp4;
CvMat* temp5;
}
CvKalman;
/*********************** Haar-like Object Detection structures **************************/
#define CV_HAAR_MAGIC_VAL 0x42500000
#define CV_TYPE_NAME_HAAR "opencv-haar-classifier"
#define CV_IS_HAAR_CLASSIFIER( haar ) \
((haar) != NULL && \
(((const CvHaarClassifierCascade*)(haar))->flags & CV_MAGIC_MASK)==CV_HAAR_MAGIC_VAL)
#define CV_HAAR_FEATURE_MAX 3
typedef struct CvHaarFeature
{
int tilted;
struct
{
CvRect r;
float weight;
} rect[CV_HAAR_FEATURE_MAX];
}
CvHaarFeature;
typedef struct CvHaarClassifier
{
int count;
CvHaarFeature* haar_feature;
float* threshold;
int* left;
int* right;
float* alpha;
}
CvHaarClassifier;
typedef struct CvHaarStageClassifier
{
int count;
float threshold;
CvHaarClassifier* classifier;
int next;
int child;
int parent;
}
CvHaarStageClassifier;
typedef struct CvHidHaarClassifierCascade CvHidHaarClassifierCascade;
typedef struct CvHaarClassifierCascade
{
int flags;
int count;
CvSize orig_window_size;
CvSize real_window_size;
double scale;
CvHaarStageClassifier* stage_classifier;
CvHidHaarClassifierCascade* hid_cascade;
}
CvHaarClassifierCascade;
typedef struct CvAvgComp
{
CvRect rect;
int neighbors;
}
CvAvgComp;
struct CvFeatureTree;
#endif /*_CVTYPES_H_*/
/* End of file. */