1/*M/////////////////////////////////////////////////////////////////////////////////////// 2// 3// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. 4// 5// By downloading, copying, installing or using the software you agree to this license. 6// If you do not agree to this license, do not download, install, 7// copy or use the software. 8// 9// 10// Intel License Agreement 11// For Open Source Computer Vision Library 12// 13// Copyright (C) 2000, Intel Corporation, all rights reserved. 14// Third party copyrights are property of their respective owners. 15// 16// Redistribution and use in source and binary forms, with or without modification, 17// are permitted provided that the following conditions are met: 18// 19// * Redistribution's of source code must retain the above copyright notice, 20// this list of conditions and the following disclaimer. 21// 22// * Redistribution's in binary form must reproduce the above copyright notice, 23// this list of conditions and the following disclaimer in the documentation 24// and/or other materials provided with the distribution. 25// 26// * The name of Intel Corporation may not be used to endorse or promote products 27// derived from this software without specific prior written permission. 28// 29// This software is provided by the copyright holders and contributors "as is" and 30// any express or implied warranties, including, but not limited to, the implied 31// warranties of merchantability and fitness for a particular purpose are disclaimed. 32// In no event shall the Intel Corporation or contributors be liable for any direct, 33// indirect, incidental, special, exemplary, or consequential damages 34// (including, but not limited to, procurement of substitute goods or services; 35// loss of use, data, or profits; or business interruption) however caused 36// and on any theory of liability, whether in contract, strict liability, 37// or tort (including negligence or otherwise) arising in any way out of 38// the use of this software, even if advised of the possibility of such damage. 39// 40//M*/ 41 42#ifndef _CVTYPES_H_ 43#define _CVTYPES_H_ 44 45#ifndef SKIP_INCLUDES 46 #include <assert.h> 47 #include <stdlib.h> 48#endif 49 50/* spatial and central moments */ 51typedef struct CvMoments 52{ 53 double m00, m10, m01, m20, m11, m02, m30, m21, m12, m03; /* spatial moments */ 54 double mu20, mu11, mu02, mu30, mu21, mu12, mu03; /* central moments */ 55 double inv_sqrt_m00; /* m00 != 0 ? 1/sqrt(m00) : 0 */ 56} 57CvMoments; 58 59/* Hu invariants */ 60typedef struct CvHuMoments 61{ 62 double hu1, hu2, hu3, hu4, hu5, hu6, hu7; /* Hu invariants */ 63} 64CvHuMoments; 65 66/**************************** Connected Component **************************************/ 67 68typedef struct CvConnectedComp 69{ 70 double area; /* area of the connected component */ 71 CvScalar value; /* average color of the connected component */ 72 CvRect rect; /* ROI of the component */ 73 CvSeq* contour; /* optional component boundary 74 (the contour might have child contours corresponding to the holes)*/ 75} 76CvConnectedComp; 77 78/* 79Internal structure that is used for sequental retrieving contours from the image. 80It supports both hierarchical and plane variants of Suzuki algorithm. 81*/ 82typedef struct _CvContourScanner* CvContourScanner; 83 84/* contour retrieval mode */ 85#define CV_RETR_EXTERNAL 0 86#define CV_RETR_LIST 1 87#define CV_RETR_CCOMP 2 88#define CV_RETR_TREE 3 89 90/* contour approximation method */ 91#define CV_CHAIN_CODE 0 92#define CV_CHAIN_APPROX_NONE 1 93#define CV_CHAIN_APPROX_SIMPLE 2 94#define CV_CHAIN_APPROX_TC89_L1 3 95#define CV_CHAIN_APPROX_TC89_KCOS 4 96#define CV_LINK_RUNS 5 97 98/* Freeman chain reader state */ 99typedef struct CvChainPtReader 100{ 101 CV_SEQ_READER_FIELDS() 102 char code; 103 CvPoint pt; 104 schar deltas[8][2]; 105} 106CvChainPtReader; 107 108/* initializes 8-element array for fast access to 3x3 neighborhood of a pixel */ 109#define CV_INIT_3X3_DELTAS( deltas, step, nch ) \ 110 ((deltas)[0] = (nch), (deltas)[1] = -(step) + (nch), \ 111 (deltas)[2] = -(step), (deltas)[3] = -(step) - (nch), \ 112 (deltas)[4] = -(nch), (deltas)[5] = (step) - (nch), \ 113 (deltas)[6] = (step), (deltas)[7] = (step) + (nch)) 114 115/* Contour tree header */ 116typedef struct CvContourTree 117{ 118 CV_SEQUENCE_FIELDS() 119 CvPoint p1; /* the first point of the binary tree root segment */ 120 CvPoint p2; /* the last point of the binary tree root segment */ 121} 122CvContourTree; 123 124/* Finds a sequence of convexity defects of given contour */ 125typedef struct CvConvexityDefect 126{ 127 CvPoint* start; /* point of the contour where the defect begins */ 128 CvPoint* end; /* point of the contour where the defect ends */ 129 CvPoint* depth_point; /* the farthest from the convex hull point within the defect */ 130 float depth; /* distance between the farthest point and the convex hull */ 131} 132CvConvexityDefect; 133 134/************ Data structures and related enumerations for Planar Subdivisions ************/ 135 136typedef size_t CvSubdiv2DEdge; 137 138#define CV_QUADEDGE2D_FIELDS() \ 139 int flags; \ 140 struct CvSubdiv2DPoint* pt[4]; \ 141 CvSubdiv2DEdge next[4]; 142 143#define CV_SUBDIV2D_POINT_FIELDS()\ 144 int flags; \ 145 CvSubdiv2DEdge first; \ 146 CvPoint2D32f pt; 147 148#define CV_SUBDIV2D_VIRTUAL_POINT_FLAG (1 << 30) 149 150typedef struct CvQuadEdge2D 151{ 152 CV_QUADEDGE2D_FIELDS() 153} 154CvQuadEdge2D; 155 156typedef struct CvSubdiv2DPoint 157{ 158 CV_SUBDIV2D_POINT_FIELDS() 159} 160CvSubdiv2DPoint; 161 162#define CV_SUBDIV2D_FIELDS() \ 163 CV_GRAPH_FIELDS() \ 164 int quad_edges; \ 165 int is_geometry_valid; \ 166 CvSubdiv2DEdge recent_edge; \ 167 CvPoint2D32f topleft; \ 168 CvPoint2D32f bottomright; 169 170typedef struct CvSubdiv2D 171{ 172 CV_SUBDIV2D_FIELDS() 173} 174CvSubdiv2D; 175 176 177typedef enum CvSubdiv2DPointLocation 178{ 179 CV_PTLOC_ERROR = -2, 180 CV_PTLOC_OUTSIDE_RECT = -1, 181 CV_PTLOC_INSIDE = 0, 182 CV_PTLOC_VERTEX = 1, 183 CV_PTLOC_ON_EDGE = 2 184} 185CvSubdiv2DPointLocation; 186 187typedef enum CvNextEdgeType 188{ 189 CV_NEXT_AROUND_ORG = 0x00, 190 CV_NEXT_AROUND_DST = 0x22, 191 CV_PREV_AROUND_ORG = 0x11, 192 CV_PREV_AROUND_DST = 0x33, 193 CV_NEXT_AROUND_LEFT = 0x13, 194 CV_NEXT_AROUND_RIGHT = 0x31, 195 CV_PREV_AROUND_LEFT = 0x20, 196 CV_PREV_AROUND_RIGHT = 0x02 197} 198CvNextEdgeType; 199 200/* get the next edge with the same origin point (counterwise) */ 201#define CV_SUBDIV2D_NEXT_EDGE( edge ) (((CvQuadEdge2D*)((edge) & ~3))->next[(edge)&3]) 202 203 204/* Defines for Distance Transform */ 205#define CV_DIST_USER -1 /* User defined distance */ 206#define CV_DIST_L1 1 /* distance = |x1-x2| + |y1-y2| */ 207#define CV_DIST_L2 2 /* the simple euclidean distance */ 208#define CV_DIST_C 3 /* distance = max(|x1-x2|,|y1-y2|) */ 209#define CV_DIST_L12 4 /* L1-L2 metric: distance = 2(sqrt(1+x*x/2) - 1)) */ 210#define CV_DIST_FAIR 5 /* distance = c^2(|x|/c-log(1+|x|/c)), c = 1.3998 */ 211#define CV_DIST_WELSCH 6 /* distance = c^2/2(1-exp(-(x/c)^2)), c = 2.9846 */ 212#define CV_DIST_HUBER 7 /* distance = |x|<c ? x^2/2 : c(|x|-c/2), c=1.345 */ 213 214 215/* Filters used in pyramid decomposition */ 216typedef enum CvFilter 217{ 218 CV_GAUSSIAN_5x5 = 7 219} 220CvFilter; 221 222/****************************************************************************************/ 223/* Older definitions */ 224/****************************************************************************************/ 225 226typedef float* CvVect32f; 227typedef float* CvMatr32f; 228typedef double* CvVect64d; 229typedef double* CvMatr64d; 230 231typedef struct CvMatrix3 232{ 233 float m[3][3]; 234} 235CvMatrix3; 236 237 238#ifdef __cplusplus 239extern "C" { 240#endif 241 242typedef float (CV_CDECL * CvDistanceFunction)( const float* a, const float* b, void* user_param ); 243 244#ifdef __cplusplus 245} 246#endif 247 248typedef struct CvConDensation 249{ 250 int MP; 251 int DP; 252 float* DynamMatr; /* Matrix of the linear Dynamics system */ 253 float* State; /* Vector of State */ 254 int SamplesNum; /* Number of the Samples */ 255 float** flSamples; /* arr of the Sample Vectors */ 256 float** flNewSamples; /* temporary array of the Sample Vectors */ 257 float* flConfidence; /* Confidence for each Sample */ 258 float* flCumulative; /* Cumulative confidence */ 259 float* Temp; /* Temporary vector */ 260 float* RandomSample; /* RandomVector to update sample set */ 261 struct CvRandState* RandS; /* Array of structures to generate random vectors */ 262} 263CvConDensation; 264 265/* 266standard Kalman filter (in G. Welch' and G. Bishop's notation): 267 268 x(k)=A*x(k-1)+B*u(k)+w(k) p(w)~N(0,Q) 269 z(k)=H*x(k)+v(k), p(v)~N(0,R) 270*/ 271typedef struct CvKalman 272{ 273 int MP; /* number of measurement vector dimensions */ 274 int DP; /* number of state vector dimensions */ 275 int CP; /* number of control vector dimensions */ 276 277 /* backward compatibility fields */ 278#if 1 279 float* PosterState; /* =state_pre->data.fl */ 280 float* PriorState; /* =state_post->data.fl */ 281 float* DynamMatr; /* =transition_matrix->data.fl */ 282 float* MeasurementMatr; /* =measurement_matrix->data.fl */ 283 float* MNCovariance; /* =measurement_noise_cov->data.fl */ 284 float* PNCovariance; /* =process_noise_cov->data.fl */ 285 float* KalmGainMatr; /* =gain->data.fl */ 286 float* PriorErrorCovariance;/* =error_cov_pre->data.fl */ 287 float* PosterErrorCovariance;/* =error_cov_post->data.fl */ 288 float* Temp1; /* temp1->data.fl */ 289 float* Temp2; /* temp2->data.fl */ 290#endif 291 292 CvMat* state_pre; /* predicted state (x'(k)): 293 x(k)=A*x(k-1)+B*u(k) */ 294 CvMat* state_post; /* corrected state (x(k)): 295 x(k)=x'(k)+K(k)*(z(k)-H*x'(k)) */ 296 CvMat* transition_matrix; /* state transition matrix (A) */ 297 CvMat* control_matrix; /* control matrix (B) 298 (it is not used if there is no control)*/ 299 CvMat* measurement_matrix; /* measurement matrix (H) */ 300 CvMat* process_noise_cov; /* process noise covariance matrix (Q) */ 301 CvMat* measurement_noise_cov; /* measurement noise covariance matrix (R) */ 302 CvMat* error_cov_pre; /* priori error estimate covariance matrix (P'(k)): 303 P'(k)=A*P(k-1)*At + Q)*/ 304 CvMat* gain; /* Kalman gain matrix (K(k)): 305 K(k)=P'(k)*Ht*inv(H*P'(k)*Ht+R)*/ 306 CvMat* error_cov_post; /* posteriori error estimate covariance matrix (P(k)): 307 P(k)=(I-K(k)*H)*P'(k) */ 308 CvMat* temp1; /* temporary matrices */ 309 CvMat* temp2; 310 CvMat* temp3; 311 CvMat* temp4; 312 CvMat* temp5; 313} 314CvKalman; 315 316 317/*********************** Haar-like Object Detection structures **************************/ 318#define CV_HAAR_MAGIC_VAL 0x42500000 319#define CV_TYPE_NAME_HAAR "opencv-haar-classifier" 320 321#define CV_IS_HAAR_CLASSIFIER( haar ) \ 322 ((haar) != NULL && \ 323 (((const CvHaarClassifierCascade*)(haar))->flags & CV_MAGIC_MASK)==CV_HAAR_MAGIC_VAL) 324 325#define CV_HAAR_FEATURE_MAX 3 326 327typedef struct CvHaarFeature 328{ 329 int tilted; 330 struct 331 { 332 CvRect r; 333 float weight; 334 } rect[CV_HAAR_FEATURE_MAX]; 335} 336CvHaarFeature; 337 338typedef struct CvHaarClassifier 339{ 340 int count; 341 CvHaarFeature* haar_feature; 342 float* threshold; 343 int* left; 344 int* right; 345 float* alpha; 346} 347CvHaarClassifier; 348 349typedef struct CvHaarStageClassifier 350{ 351 int count; 352 float threshold; 353 CvHaarClassifier* classifier; 354 355 int next; 356 int child; 357 int parent; 358} 359CvHaarStageClassifier; 360 361typedef struct CvHidHaarClassifierCascade CvHidHaarClassifierCascade; 362 363typedef struct CvHaarClassifierCascade 364{ 365 int flags; 366 int count; 367 CvSize orig_window_size; 368 CvSize real_window_size; 369 double scale; 370 CvHaarStageClassifier* stage_classifier; 371 CvHidHaarClassifierCascade* hid_cascade; 372} 373CvHaarClassifierCascade; 374 375typedef struct CvAvgComp 376{ 377 CvRect rect; 378 int neighbors; 379} 380CvAvgComp; 381 382struct CvFeatureTree; 383 384#endif /*_CVTYPES_H_*/ 385 386/* End of file. */ 387