This file is indexed.

/usr/include/opencv2/legacy/legacy.hpp is in libopencv-legacy-dev 2.4.9.1+dfsg1-2.

This file is owned by root:root, with mode 0o644.

The actual contents of the file can be viewed below.

   1
   2
   3
   4
   5
   6
   7
   8
   9
  10
  11
  12
  13
  14
  15
  16
  17
  18
  19
  20
  21
  22
  23
  24
  25
  26
  27
  28
  29
  30
  31
  32
  33
  34
  35
  36
  37
  38
  39
  40
  41
  42
  43
  44
  45
  46
  47
  48
  49
  50
  51
  52
  53
  54
  55
  56
  57
  58
  59
  60
  61
  62
  63
  64
  65
  66
  67
  68
  69
  70
  71
  72
  73
  74
  75
  76
  77
  78
  79
  80
  81
  82
  83
  84
  85
  86
  87
  88
  89
  90
  91
  92
  93
  94
  95
  96
  97
  98
  99
 100
 101
 102
 103
 104
 105
 106
 107
 108
 109
 110
 111
 112
 113
 114
 115
 116
 117
 118
 119
 120
 121
 122
 123
 124
 125
 126
 127
 128
 129
 130
 131
 132
 133
 134
 135
 136
 137
 138
 139
 140
 141
 142
 143
 144
 145
 146
 147
 148
 149
 150
 151
 152
 153
 154
 155
 156
 157
 158
 159
 160
 161
 162
 163
 164
 165
 166
 167
 168
 169
 170
 171
 172
 173
 174
 175
 176
 177
 178
 179
 180
 181
 182
 183
 184
 185
 186
 187
 188
 189
 190
 191
 192
 193
 194
 195
 196
 197
 198
 199
 200
 201
 202
 203
 204
 205
 206
 207
 208
 209
 210
 211
 212
 213
 214
 215
 216
 217
 218
 219
 220
 221
 222
 223
 224
 225
 226
 227
 228
 229
 230
 231
 232
 233
 234
 235
 236
 237
 238
 239
 240
 241
 242
 243
 244
 245
 246
 247
 248
 249
 250
 251
 252
 253
 254
 255
 256
 257
 258
 259
 260
 261
 262
 263
 264
 265
 266
 267
 268
 269
 270
 271
 272
 273
 274
 275
 276
 277
 278
 279
 280
 281
 282
 283
 284
 285
 286
 287
 288
 289
 290
 291
 292
 293
 294
 295
 296
 297
 298
 299
 300
 301
 302
 303
 304
 305
 306
 307
 308
 309
 310
 311
 312
 313
 314
 315
 316
 317
 318
 319
 320
 321
 322
 323
 324
 325
 326
 327
 328
 329
 330
 331
 332
 333
 334
 335
 336
 337
 338
 339
 340
 341
 342
 343
 344
 345
 346
 347
 348
 349
 350
 351
 352
 353
 354
 355
 356
 357
 358
 359
 360
 361
 362
 363
 364
 365
 366
 367
 368
 369
 370
 371
 372
 373
 374
 375
 376
 377
 378
 379
 380
 381
 382
 383
 384
 385
 386
 387
 388
 389
 390
 391
 392
 393
 394
 395
 396
 397
 398
 399
 400
 401
 402
 403
 404
 405
 406
 407
 408
 409
 410
 411
 412
 413
 414
 415
 416
 417
 418
 419
 420
 421
 422
 423
 424
 425
 426
 427
 428
 429
 430
 431
 432
 433
 434
 435
 436
 437
 438
 439
 440
 441
 442
 443
 444
 445
 446
 447
 448
 449
 450
 451
 452
 453
 454
 455
 456
 457
 458
 459
 460
 461
 462
 463
 464
 465
 466
 467
 468
 469
 470
 471
 472
 473
 474
 475
 476
 477
 478
 479
 480
 481
 482
 483
 484
 485
 486
 487
 488
 489
 490
 491
 492
 493
 494
 495
 496
 497
 498
 499
 500
 501
 502
 503
 504
 505
 506
 507
 508
 509
 510
 511
 512
 513
 514
 515
 516
 517
 518
 519
 520
 521
 522
 523
 524
 525
 526
 527
 528
 529
 530
 531
 532
 533
 534
 535
 536
 537
 538
 539
 540
 541
 542
 543
 544
 545
 546
 547
 548
 549
 550
 551
 552
 553
 554
 555
 556
 557
 558
 559
 560
 561
 562
 563
 564
 565
 566
 567
 568
 569
 570
 571
 572
 573
 574
 575
 576
 577
 578
 579
 580
 581
 582
 583
 584
 585
 586
 587
 588
 589
 590
 591
 592
 593
 594
 595
 596
 597
 598
 599
 600
 601
 602
 603
 604
 605
 606
 607
 608
 609
 610
 611
 612
 613
 614
 615
 616
 617
 618
 619
 620
 621
 622
 623
 624
 625
 626
 627
 628
 629
 630
 631
 632
 633
 634
 635
 636
 637
 638
 639
 640
 641
 642
 643
 644
 645
 646
 647
 648
 649
 650
 651
 652
 653
 654
 655
 656
 657
 658
 659
 660
 661
 662
 663
 664
 665
 666
 667
 668
 669
 670
 671
 672
 673
 674
 675
 676
 677
 678
 679
 680
 681
 682
 683
 684
 685
 686
 687
 688
 689
 690
 691
 692
 693
 694
 695
 696
 697
 698
 699
 700
 701
 702
 703
 704
 705
 706
 707
 708
 709
 710
 711
 712
 713
 714
 715
 716
 717
 718
 719
 720
 721
 722
 723
 724
 725
 726
 727
 728
 729
 730
 731
 732
 733
 734
 735
 736
 737
 738
 739
 740
 741
 742
 743
 744
 745
 746
 747
 748
 749
 750
 751
 752
 753
 754
 755
 756
 757
 758
 759
 760
 761
 762
 763
 764
 765
 766
 767
 768
 769
 770
 771
 772
 773
 774
 775
 776
 777
 778
 779
 780
 781
 782
 783
 784
 785
 786
 787
 788
 789
 790
 791
 792
 793
 794
 795
 796
 797
 798
 799
 800
 801
 802
 803
 804
 805
 806
 807
 808
 809
 810
 811
 812
 813
 814
 815
 816
 817
 818
 819
 820
 821
 822
 823
 824
 825
 826
 827
 828
 829
 830
 831
 832
 833
 834
 835
 836
 837
 838
 839
 840
 841
 842
 843
 844
 845
 846
 847
 848
 849
 850
 851
 852
 853
 854
 855
 856
 857
 858
 859
 860
 861
 862
 863
 864
 865
 866
 867
 868
 869
 870
 871
 872
 873
 874
 875
 876
 877
 878
 879
 880
 881
 882
 883
 884
 885
 886
 887
 888
 889
 890
 891
 892
 893
 894
 895
 896
 897
 898
 899
 900
 901
 902
 903
 904
 905
 906
 907
 908
 909
 910
 911
 912
 913
 914
 915
 916
 917
 918
 919
 920
 921
 922
 923
 924
 925
 926
 927
 928
 929
 930
 931
 932
 933
 934
 935
 936
 937
 938
 939
 940
 941
 942
 943
 944
 945
 946
 947
 948
 949
 950
 951
 952
 953
 954
 955
 956
 957
 958
 959
 960
 961
 962
 963
 964
 965
 966
 967
 968
 969
 970
 971
 972
 973
 974
 975
 976
 977
 978
 979
 980
 981
 982
 983
 984
 985
 986
 987
 988
 989
 990
 991
 992
 993
 994
 995
 996
 997
 998
 999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
1192
1193
1194
1195
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
1266
1267
1268
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
1290
1291
1292
1293
1294
1295
1296
1297
1298
1299
1300
1301
1302
1303
1304
1305
1306
1307
1308
1309
1310
1311
1312
1313
1314
1315
1316
1317
1318
1319
1320
1321
1322
1323
1324
1325
1326
1327
1328
1329
1330
1331
1332
1333
1334
1335
1336
1337
1338
1339
1340
1341
1342
1343
1344
1345
1346
1347
1348
1349
1350
1351
1352
1353
1354
1355
1356
1357
1358
1359
1360
1361
1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
1385
1386
1387
1388
1389
1390
1391
1392
1393
1394
1395
1396
1397
1398
1399
1400
1401
1402
1403
1404
1405
1406
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
1424
1425
1426
1427
1428
1429
1430
1431
1432
1433
1434
1435
1436
1437
1438
1439
1440
1441
1442
1443
1444
1445
1446
1447
1448
1449
1450
1451
1452
1453
1454
1455
1456
1457
1458
1459
1460
1461
1462
1463
1464
1465
1466
1467
1468
1469
1470
1471
1472
1473
1474
1475
1476
1477
1478
1479
1480
1481
1482
1483
1484
1485
1486
1487
1488
1489
1490
1491
1492
1493
1494
1495
1496
1497
1498
1499
1500
1501
1502
1503
1504
1505
1506
1507
1508
1509
1510
1511
1512
1513
1514
1515
1516
1517
1518
1519
1520
1521
1522
1523
1524
1525
1526
1527
1528
1529
1530
1531
1532
1533
1534
1535
1536
1537
1538
1539
1540
1541
1542
1543
1544
1545
1546
1547
1548
1549
1550
1551
1552
1553
1554
1555
1556
1557
1558
1559
1560
1561
1562
1563
1564
1565
1566
1567
1568
1569
1570
1571
1572
1573
1574
1575
1576
1577
1578
1579
1580
1581
1582
1583
1584
1585
1586
1587
1588
1589
1590
1591
1592
1593
1594
1595
1596
1597
1598
1599
1600
1601
1602
1603
1604
1605
1606
1607
1608
1609
1610
1611
1612
1613
1614
1615
1616
1617
1618
1619
1620
1621
1622
1623
1624
1625
1626
1627
1628
1629
1630
1631
1632
1633
1634
1635
1636
1637
1638
1639
1640
1641
1642
1643
1644
1645
1646
1647
1648
1649
1650
1651
1652
1653
1654
1655
1656
1657
1658
1659
1660
1661
1662
1663
1664
1665
1666
1667
1668
1669
1670
1671
1672
1673
1674
1675
1676
1677
1678
1679
1680
1681
1682
1683
1684
1685
1686
1687
1688
1689
1690
1691
1692
1693
1694
1695
1696
1697
1698
1699
1700
1701
1702
1703
1704
1705
1706
1707
1708
1709
1710
1711
1712
1713
1714
1715
1716
1717
1718
1719
1720
1721
1722
1723
1724
1725
1726
1727
1728
1729
1730
1731
1732
1733
1734
1735
1736
1737
1738
1739
1740
1741
1742
1743
1744
1745
1746
1747
1748
1749
1750
1751
1752
1753
1754
1755
1756
1757
1758
1759
1760
1761
1762
1763
1764
1765
1766
1767
1768
1769
1770
1771
1772
1773
1774
1775
1776
1777
1778
1779
1780
1781
1782
1783
1784
1785
1786
1787
1788
1789
1790
1791
1792
1793
1794
1795
1796
1797
1798
1799
1800
1801
1802
1803
1804
1805
1806
1807
1808
1809
1810
1811
1812
1813
1814
1815
1816
1817
1818
1819
1820
1821
1822
1823
1824
1825
1826
1827
1828
1829
1830
1831
1832
1833
1834
1835
1836
1837
1838
1839
1840
1841
1842
1843
1844
1845
1846
1847
1848
1849
1850
1851
1852
1853
1854
1855
1856
1857
1858
1859
1860
1861
1862
1863
1864
1865
1866
1867
1868
1869
1870
1871
1872
1873
1874
1875
1876
1877
1878
1879
1880
1881
1882
1883
1884
1885
1886
1887
1888
1889
1890
1891
1892
1893
1894
1895
1896
1897
1898
1899
1900
1901
1902
1903
1904
1905
1906
1907
1908
1909
1910
1911
1912
1913
1914
1915
1916
1917
1918
1919
1920
1921
1922
1923
1924
1925
1926
1927
1928
1929
1930
1931
1932
1933
1934
1935
1936
1937
1938
1939
1940
1941
1942
1943
1944
1945
1946
1947
1948
1949
1950
1951
1952
1953
1954
1955
1956
1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
2041
2042
2043
2044
2045
2046
2047
2048
2049
2050
2051
2052
2053
2054
2055
2056
2057
2058
2059
2060
2061
2062
2063
2064
2065
2066
2067
2068
2069
2070
2071
2072
2073
2074
2075
2076
2077
2078
2079
2080
2081
2082
2083
2084
2085
2086
2087
2088
2089
2090
2091
2092
2093
2094
2095
2096
2097
2098
2099
2100
2101
2102
2103
2104
2105
2106
2107
2108
2109
2110
2111
2112
2113
2114
2115
2116
2117
2118
2119
2120
2121
2122
2123
2124
2125
2126
2127
2128
2129
2130
2131
2132
2133
2134
2135
2136
2137
2138
2139
2140
2141
2142
2143
2144
2145
2146
2147
2148
2149
2150
2151
2152
2153
2154
2155
2156
2157
2158
2159
2160
2161
2162
2163
2164
2165
2166
2167
2168
2169
2170
2171
2172
2173
2174
2175
2176
2177
2178
2179
2180
2181
2182
2183
2184
2185
2186
2187
2188
2189
2190
2191
2192
2193
2194
2195
2196
2197
2198
2199
2200
2201
2202
2203
2204
2205
2206
2207
2208
2209
2210
2211
2212
2213
2214
2215
2216
2217
2218
2219
2220
2221
2222
2223
2224
2225
2226
2227
2228
2229
2230
2231
2232
2233
2234
2235
2236
2237
2238
2239
2240
2241
2242
2243
2244
2245
2246
2247
2248
2249
2250
2251
2252
2253
2254
2255
2256
2257
2258
2259
2260
2261
2262
2263
2264
2265
2266
2267
2268
2269
2270
2271
2272
2273
2274
2275
2276
2277
2278
2279
2280
2281
2282
2283
2284
2285
2286
2287
2288
2289
2290
2291
2292
2293
2294
2295
2296
2297
2298
2299
2300
2301
2302
2303
2304
2305
2306
2307
2308
2309
2310
2311
2312
2313
2314
2315
2316
2317
2318
2319
2320
2321
2322
2323
2324
2325
2326
2327
2328
2329
2330
2331
2332
2333
2334
2335
2336
2337
2338
2339
2340
2341
2342
2343
2344
2345
2346
2347
2348
2349
2350
2351
2352
2353
2354
2355
2356
2357
2358
2359
2360
2361
2362
2363
2364
2365
2366
2367
2368
2369
2370
2371
2372
2373
2374
2375
2376
2377
2378
2379
2380
2381
2382
2383
2384
2385
2386
2387
2388
2389
2390
2391
2392
2393
2394
2395
2396
2397
2398
2399
2400
2401
2402
2403
2404
2405
2406
2407
2408
2409
2410
2411
2412
2413
2414
2415
2416
2417
2418
2419
2420
2421
2422
2423
2424
2425
2426
2427
2428
2429
2430
2431
2432
2433
2434
2435
2436
2437
2438
2439
2440
2441
2442
2443
2444
2445
2446
2447
2448
2449
2450
2451
2452
2453
2454
2455
2456
2457
2458
2459
2460
2461
2462
2463
2464
2465
2466
2467
2468
2469
2470
2471
2472
2473
2474
2475
2476
2477
2478
2479
2480
2481
2482
2483
2484
2485
2486
2487
2488
2489
2490
2491
2492
2493
2494
2495
2496
2497
2498
2499
2500
2501
2502
2503
2504
2505
2506
2507
2508
2509
2510
2511
2512
2513
2514
2515
2516
2517
2518
2519
2520
2521
2522
2523
2524
2525
2526
2527
2528
2529
2530
2531
2532
2533
2534
2535
2536
2537
2538
2539
2540
2541
2542
2543
2544
2545
2546
2547
2548
2549
2550
2551
2552
2553
2554
2555
2556
2557
2558
2559
2560
2561
2562
2563
2564
2565
2566
2567
2568
2569
2570
2571
2572
2573
2574
2575
2576
2577
2578
2579
2580
2581
2582
2583
2584
2585
2586
2587
2588
2589
2590
2591
2592
2593
2594
2595
2596
2597
2598
2599
2600
2601
2602
2603
2604
2605
2606
2607
2608
2609
2610
2611
2612
2613
2614
2615
2616
2617
2618
2619
2620
2621
2622
2623
2624
2625
2626
2627
2628
2629
2630
2631
2632
2633
2634
2635
2636
2637
2638
2639
2640
2641
2642
2643
2644
2645
2646
2647
2648
2649
2650
2651
2652
2653
2654
2655
2656
2657
2658
2659
2660
2661
2662
2663
2664
2665
2666
2667
2668
2669
2670
2671
2672
2673
2674
2675
2676
2677
2678
2679
2680
2681
2682
2683
2684
2685
2686
2687
2688
2689
2690
2691
2692
2693
2694
2695
2696
2697
2698
2699
2700
2701
2702
2703
2704
2705
2706
2707
2708
2709
2710
2711
2712
2713
2714
2715
2716
2717
2718
2719
2720
2721
2722
2723
2724
2725
2726
2727
2728
2729
2730
2731
2732
2733
2734
2735
2736
2737
2738
2739
2740
2741
2742
2743
2744
2745
2746
2747
2748
2749
2750
2751
2752
2753
2754
2755
2756
2757
2758
2759
2760
2761
2762
2763
2764
2765
2766
2767
2768
2769
2770
2771
2772
2773
2774
2775
2776
2777
2778
2779
2780
2781
2782
2783
2784
2785
2786
2787
2788
2789
2790
2791
2792
2793
2794
2795
2796
2797
2798
2799
2800
2801
2802
2803
2804
2805
2806
2807
2808
2809
2810
2811
2812
2813
2814
2815
2816
2817
2818
2819
2820
2821
2822
2823
2824
2825
2826
2827
2828
2829
2830
2831
2832
2833
2834
2835
2836
2837
2838
2839
2840
2841
2842
2843
2844
2845
2846
2847
2848
2849
2850
2851
2852
2853
2854
2855
2856
2857
2858
2859
2860
2861
2862
2863
2864
2865
2866
2867
2868
2869
2870
2871
2872
2873
2874
2875
2876
2877
2878
2879
2880
2881
2882
2883
2884
2885
2886
2887
2888
2889
2890
2891
2892
2893
2894
2895
2896
2897
2898
2899
2900
2901
2902
2903
2904
2905
2906
2907
2908
2909
2910
2911
2912
2913
2914
2915
2916
2917
2918
2919
2920
2921
2922
2923
2924
2925
2926
2927
2928
2929
2930
2931
2932
2933
2934
2935
2936
2937
2938
2939
2940
2941
2942
2943
2944
2945
2946
2947
2948
2949
2950
2951
2952
2953
2954
2955
2956
2957
2958
2959
2960
2961
2962
2963
2964
2965
2966
2967
2968
2969
2970
2971
2972
2973
2974
2975
2976
2977
2978
2979
2980
2981
2982
2983
2984
2985
2986
2987
2988
2989
2990
2991
2992
2993
2994
2995
2996
2997
2998
2999
3000
3001
3002
3003
3004
3005
3006
3007
3008
3009
3010
3011
3012
3013
3014
3015
3016
3017
3018
3019
3020
3021
3022
3023
3024
3025
3026
3027
3028
3029
3030
3031
3032
3033
3034
3035
3036
3037
3038
3039
3040
3041
3042
3043
3044
3045
3046
3047
3048
3049
3050
3051
3052
3053
3054
3055
3056
3057
3058
3059
3060
3061
3062
3063
3064
3065
3066
3067
3068
3069
3070
3071
3072
3073
3074
3075
3076
3077
3078
3079
3080
3081
3082
3083
3084
3085
3086
3087
3088
3089
3090
3091
3092
3093
3094
3095
3096
3097
3098
3099
3100
3101
3102
3103
3104
3105
3106
3107
3108
3109
3110
3111
3112
3113
3114
3115
3116
3117
3118
3119
3120
3121
3122
3123
3124
3125
3126
3127
3128
3129
3130
3131
3132
3133
3134
3135
3136
3137
3138
3139
3140
3141
3142
3143
3144
3145
3146
3147
3148
3149
3150
3151
3152
3153
3154
3155
3156
3157
3158
3159
3160
3161
3162
3163
3164
3165
3166
3167
3168
3169
3170
3171
3172
3173
3174
3175
3176
3177
3178
3179
3180
3181
3182
3183
3184
3185
3186
3187
3188
3189
3190
3191
3192
3193
3194
3195
3196
3197
3198
3199
3200
3201
3202
3203
3204
3205
3206
3207
3208
3209
3210
3211
3212
3213
3214
3215
3216
3217
3218
3219
3220
3221
3222
3223
3224
3225
3226
3227
3228
3229
3230
3231
3232
3233
3234
3235
3236
3237
3238
3239
3240
3241
3242
3243
3244
3245
3246
3247
3248
3249
3250
3251
3252
3253
3254
3255
3256
3257
3258
3259
3260
3261
3262
3263
3264
3265
3266
3267
3268
3269
3270
3271
3272
3273
3274
3275
3276
3277
3278
3279
3280
3281
3282
3283
3284
3285
3286
3287
3288
3289
3290
3291
3292
3293
3294
3295
3296
3297
3298
3299
3300
3301
3302
3303
3304
3305
3306
3307
3308
3309
3310
3311
3312
3313
3314
3315
3316
3317
3318
3319
3320
3321
3322
3323
3324
3325
3326
3327
3328
3329
3330
3331
3332
3333
3334
3335
3336
3337
3338
3339
3340
3341
3342
3343
3344
3345
3346
3347
3348
3349
3350
3351
3352
3353
3354
3355
3356
3357
3358
3359
3360
3361
3362
3363
3364
3365
3366
3367
3368
3369
3370
3371
3372
3373
3374
3375
3376
3377
3378
3379
3380
3381
3382
3383
3384
3385
3386
3387
3388
3389
3390
3391
3392
3393
3394
3395
3396
3397
3398
3399
3400
3401
3402
3403
3404
3405
3406
3407
3408
3409
3410
3411
3412
3413
3414
3415
3416
3417
3418
3419
3420
3421
3422
3423
3424
3425
3426
3427
3428
3429
3430
3431
3432
3433
3434
3435
3436
/*M///////////////////////////////////////////////////////////////////////////////////////
//
//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
//  By downloading, copying, installing or using the software you agree to this license.
//  If you do not agree to this license, do not download, install,
//  copy or use the software.
//
//
//                        Intel License Agreement
//                For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
//   * Redistribution's of source code must retain the above copyright notice,
//     this list of conditions and the following disclaimer.
//
//   * Redistribution's in binary form must reproduce the above copyright notice,
//     this list of conditions and the following disclaimer in the documentation
//     and/or other materials provided with the distribution.
//
//   * The name of Intel Corporation may not be used to endorse or promote products
//     derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/

#ifndef __OPENCV_LEGACY_HPP__
#define __OPENCV_LEGACY_HPP__

#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/imgproc/imgproc_c.h"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/calib3d/calib3d.hpp"
#include "opencv2/ml/ml.hpp"

#ifdef __cplusplus
extern "C" {
#endif

CVAPI(CvSeq*) cvSegmentImage( const CvArr* srcarr, CvArr* dstarr,
                                    double canny_threshold,
                                    double ffill_threshold,
                                    CvMemStorage* storage );

/****************************************************************************************\
*                                  Eigen objects                                         *
\****************************************************************************************/

typedef int (CV_CDECL * CvCallback)(int index, void* buffer, void* user_data);
typedef union
{
    CvCallback callback;
    void* data;
}
CvInput;

#define CV_EIGOBJ_NO_CALLBACK     0
#define CV_EIGOBJ_INPUT_CALLBACK  1
#define CV_EIGOBJ_OUTPUT_CALLBACK 2
#define CV_EIGOBJ_BOTH_CALLBACK   3

/* Calculates covariation matrix of a set of arrays */
CVAPI(void)  cvCalcCovarMatrixEx( int nObjects, void* input, int ioFlags,
                                  int ioBufSize, uchar* buffer, void* userData,
                                  IplImage* avg, float* covarMatrix );

/* Calculates eigen values and vectors of covariation matrix of a set of
   arrays */
CVAPI(void)  cvCalcEigenObjects( int nObjects, void* input, void* output,
                                 int ioFlags, int ioBufSize, void* userData,
                                 CvTermCriteria* calcLimit, IplImage* avg,
                                 float* eigVals );

/* Calculates dot product (obj - avg) * eigObj (i.e. projects image to eigen vector) */
CVAPI(double)  cvCalcDecompCoeff( IplImage* obj, IplImage* eigObj, IplImage* avg );

/* Projects image to eigen space (finds all decomposion coefficients */
CVAPI(void)  cvEigenDecomposite( IplImage* obj, int nEigObjs, void* eigInput,
                                 int ioFlags, void* userData, IplImage* avg,
                                 float* coeffs );

/* Projects original objects used to calculate eigen space basis to that space */
CVAPI(void)  cvEigenProjection( void* eigInput, int nEigObjs, int ioFlags,
                                void* userData, float* coeffs, IplImage* avg,
                                IplImage* proj );

/****************************************************************************************\
*                                       1D/2D HMM                                        *
\****************************************************************************************/

typedef struct CvImgObsInfo
{
    int obs_x;
    int obs_y;
    int obs_size;
    float* obs;//consequtive observations

    int* state;/* arr of pairs superstate/state to which observation belong */
    int* mix;  /* number of mixture to which observation belong */

} CvImgObsInfo;/*struct for 1 image*/

typedef CvImgObsInfo Cv1DObsInfo;

typedef struct CvEHMMState
{
    int num_mix;        /*number of mixtures in this state*/
    float* mu;          /*mean vectors corresponding to each mixture*/
    float* inv_var;     /* square root of inversed variances corresp. to each mixture*/
    float* log_var_val; /* sum of 0.5 (LN2PI + ln(variance[i]) ) for i=1,n */
    float* weight;      /*array of mixture weights. Summ of all weights in state is 1. */

} CvEHMMState;

typedef struct CvEHMM
{
    int level; /* 0 - lowest(i.e its states are real states), ..... */
    int num_states; /* number of HMM states */
    float*  transP;/*transition probab. matrices for states */
    float** obsProb; /* if level == 0 - array of brob matrices corresponding to hmm
                        if level == 1 - martix of matrices */
    union
    {
        CvEHMMState* state; /* if level == 0 points to real states array,
                               if not - points to embedded hmms */
        struct CvEHMM* ehmm; /* pointer to an embedded model or NULL, if it is a leaf */
    } u;

} CvEHMM;

/*CVAPI(int)  icvCreate1DHMM( CvEHMM** this_hmm,
                                   int state_number, int* num_mix, int obs_size );

CVAPI(int)  icvRelease1DHMM( CvEHMM** phmm );

CVAPI(int)  icvUniform1DSegm( Cv1DObsInfo* obs_info, CvEHMM* hmm );

CVAPI(int)  icvInit1DMixSegm( Cv1DObsInfo** obs_info_array, int num_img, CvEHMM* hmm);

CVAPI(int)  icvEstimate1DHMMStateParams( CvImgObsInfo** obs_info_array, int num_img, CvEHMM* hmm);

CVAPI(int)  icvEstimate1DObsProb( CvImgObsInfo* obs_info, CvEHMM* hmm );

CVAPI(int)  icvEstimate1DTransProb( Cv1DObsInfo** obs_info_array,
                                           int num_seq,
                                           CvEHMM* hmm );

CVAPI(float)  icvViterbi( Cv1DObsInfo* obs_info, CvEHMM* hmm);

CVAPI(int)  icv1DMixSegmL2( CvImgObsInfo** obs_info_array, int num_img, CvEHMM* hmm );*/

/*********************************** Embedded HMMs *************************************/

/* Creates 2D HMM */
CVAPI(CvEHMM*)  cvCreate2DHMM( int* stateNumber, int* numMix, int obsSize );

/* Releases HMM */
CVAPI(void)  cvRelease2DHMM( CvEHMM** hmm );

#define CV_COUNT_OBS(roi, win, delta, numObs )                                       \
{                                                                                    \
   (numObs)->width  =((roi)->width  -(win)->width  +(delta)->width)/(delta)->width;  \
   (numObs)->height =((roi)->height -(win)->height +(delta)->height)/(delta)->height;\
}

/* Creates storage for observation vectors */
CVAPI(CvImgObsInfo*)  cvCreateObsInfo( CvSize numObs, int obsSize );

/* Releases storage for observation vectors */
CVAPI(void)  cvReleaseObsInfo( CvImgObsInfo** obs_info );


/* The function takes an image on input and and returns the sequnce of observations
   to be used with an embedded HMM; Each observation is top-left block of DCT
   coefficient matrix */
CVAPI(void)  cvImgToObs_DCT( const CvArr* arr, float* obs, CvSize dctSize,
                             CvSize obsSize, CvSize delta );


/* Uniformly segments all observation vectors extracted from image */
CVAPI(void)  cvUniformImgSegm( CvImgObsInfo* obs_info, CvEHMM* ehmm );

/* Does mixture segmentation of the states of embedded HMM */
CVAPI(void)  cvInitMixSegm( CvImgObsInfo** obs_info_array,
                            int num_img, CvEHMM* hmm );

/* Function calculates means, variances, weights of every Gaussian mixture
   of every low-level state of embedded HMM */
CVAPI(void)  cvEstimateHMMStateParams( CvImgObsInfo** obs_info_array,
                                       int num_img, CvEHMM* hmm );

/* Function computes transition probability matrices of embedded HMM
   given observations segmentation */
CVAPI(void)  cvEstimateTransProb( CvImgObsInfo** obs_info_array,
                                  int num_img, CvEHMM* hmm );

/* Function computes probabilities of appearing observations at any state
   (i.e. computes P(obs|state) for every pair(obs,state)) */
CVAPI(void)  cvEstimateObsProb( CvImgObsInfo* obs_info,
                                CvEHMM* hmm );

/* Runs Viterbi algorithm for embedded HMM */
CVAPI(float)  cvEViterbi( CvImgObsInfo* obs_info, CvEHMM* hmm );


/* Function clusters observation vectors from several images
   given observations segmentation.
   Euclidean distance used for clustering vectors.
   Centers of clusters are given means of every mixture */
CVAPI(void)  cvMixSegmL2( CvImgObsInfo** obs_info_array,
                          int num_img, CvEHMM* hmm );

/****************************************************************************************\
*               A few functions from old stereo gesture recognition demosions            *
\****************************************************************************************/

/* Creates hand mask image given several points on the hand */
CVAPI(void)  cvCreateHandMask( CvSeq* hand_points,
                                   IplImage *img_mask, CvRect *roi);

/* Finds hand region in range image data */
CVAPI(void)  cvFindHandRegion (CvPoint3D32f* points, int count,
                                CvSeq* indexs,
                                float* line, CvSize2D32f size, int flag,
                                CvPoint3D32f* center,
                                CvMemStorage* storage, CvSeq **numbers);

/* Finds hand region in range image data (advanced version) */
CVAPI(void)  cvFindHandRegionA( CvPoint3D32f* points, int count,
                                CvSeq* indexs,
                                float* line, CvSize2D32f size, int jc,
                                CvPoint3D32f* center,
                                CvMemStorage* storage, CvSeq **numbers);

/* Calculates the cooficients of the homography matrix */
CVAPI(void)  cvCalcImageHomography( float* line, CvPoint3D32f* center,
                                    float* intrinsic, float* homography );

/****************************************************************************************\
*                           More operations on sequences                                 *
\****************************************************************************************/

/*****************************************************************************************/

#define CV_CURRENT_INT( reader ) (*((int *)(reader).ptr))
#define CV_PREV_INT( reader ) (*((int *)(reader).prev_elem))

#define  CV_GRAPH_WEIGHTED_VERTEX_FIELDS() CV_GRAPH_VERTEX_FIELDS()\
    float weight;

#define  CV_GRAPH_WEIGHTED_EDGE_FIELDS() CV_GRAPH_EDGE_FIELDS()

typedef struct CvGraphWeightedVtx
{
    CV_GRAPH_WEIGHTED_VERTEX_FIELDS()
} CvGraphWeightedVtx;

typedef struct CvGraphWeightedEdge
{
    CV_GRAPH_WEIGHTED_EDGE_FIELDS()
} CvGraphWeightedEdge;

typedef enum CvGraphWeightType
{
    CV_NOT_WEIGHTED,
    CV_WEIGHTED_VTX,
    CV_WEIGHTED_EDGE,
    CV_WEIGHTED_ALL
} CvGraphWeightType;


/* Calculates histogram of a contour */
CVAPI(void)  cvCalcPGH( const CvSeq* contour, CvHistogram* hist );

#define CV_DOMINANT_IPAN 1

/* Finds high-curvature points of the contour */
CVAPI(CvSeq*) cvFindDominantPoints( CvSeq* contour, CvMemStorage* storage,
                                   int method CV_DEFAULT(CV_DOMINANT_IPAN),
                                   double parameter1 CV_DEFAULT(0),
                                   double parameter2 CV_DEFAULT(0),
                                   double parameter3 CV_DEFAULT(0),
                                   double parameter4 CV_DEFAULT(0));

/*****************************************************************************************/


/*******************************Stereo correspondence*************************************/

typedef struct CvCliqueFinder
{
    CvGraph* graph;
    int**    adj_matr;
    int N; //graph size

    // stacks, counters etc/
    int k; //stack size
    int* current_comp;
    int** All;

    int* ne;
    int* ce;
    int* fixp; //node with minimal disconnections
    int* nod;
    int* s; //for selected candidate
    int status;
    int best_score;
    int weighted;
    int weighted_edges;
    float best_weight;
    float* edge_weights;
    float* vertex_weights;
    float* cur_weight;
    float* cand_weight;

} CvCliqueFinder;

#define CLIQUE_TIME_OFF 2
#define CLIQUE_FOUND 1
#define CLIQUE_END   0

/*CVAPI(void) cvStartFindCliques( CvGraph* graph, CvCliqueFinder* finder, int reverse,
                                   int weighted CV_DEFAULT(0),  int weighted_edges CV_DEFAULT(0));
CVAPI(int) cvFindNextMaximalClique( CvCliqueFinder* finder, int* clock_rest CV_DEFAULT(0) );
CVAPI(void) cvEndFindCliques( CvCliqueFinder* finder );

CVAPI(void) cvBronKerbosch( CvGraph* graph );*/


/*F///////////////////////////////////////////////////////////////////////////////////////
//
//    Name:    cvSubgraphWeight
//    Purpose: finds weight of subgraph in a graph
//    Context:
//    Parameters:
//      graph - input graph.
//      subgraph - sequence of pairwise different ints.  These are indices of vertices of subgraph.
//      weight_type - describes the way we measure weight.
//            one of the following:
//            CV_NOT_WEIGHTED - weight of a clique is simply its size
//            CV_WEIGHTED_VTX - weight of a clique is the sum of weights of its vertices
//            CV_WEIGHTED_EDGE - the same but edges
//            CV_WEIGHTED_ALL - the same but both edges and vertices
//      weight_vtx - optional vector of floats, with size = graph->total.
//            If weight_type is either CV_WEIGHTED_VTX or CV_WEIGHTED_ALL
//            weights of vertices must be provided.  If weight_vtx not zero
//            these weights considered to be here, otherwise function assumes
//            that vertices of graph are inherited from CvGraphWeightedVtx.
//      weight_edge - optional matrix of floats, of width and height = graph->total.
//            If weight_type is either CV_WEIGHTED_EDGE or CV_WEIGHTED_ALL
//            weights of edges ought to be supplied.  If weight_edge is not zero
//            function finds them here, otherwise function expects
//            edges of graph to be inherited from CvGraphWeightedEdge.
//            If this parameter is not zero structure of the graph is determined from matrix
//            rather than from CvGraphEdge's.  In particular, elements corresponding to
//            absent edges should be zero.
//    Returns:
//      weight of subgraph.
//    Notes:
//F*/
/*CVAPI(float) cvSubgraphWeight( CvGraph *graph, CvSeq *subgraph,
                                  CvGraphWeightType weight_type CV_DEFAULT(CV_NOT_WEIGHTED),
                                  CvVect32f weight_vtx CV_DEFAULT(0),
                                  CvMatr32f weight_edge CV_DEFAULT(0) );*/


/*F///////////////////////////////////////////////////////////////////////////////////////
//
//    Name:    cvFindCliqueEx
//    Purpose: tries to find clique with maximum possible weight in a graph
//    Context:
//    Parameters:
//      graph - input graph.
//      storage - memory storage to be used by the result.
//      is_complementary - optional flag showing whether function should seek for clique
//            in complementary graph.
//      weight_type - describes our notion about weight.
//            one of the following:
//            CV_NOT_WEIGHTED - weight of a clique is simply its size
//            CV_WEIGHTED_VTX - weight of a clique is the sum of weights of its vertices
//            CV_WEIGHTED_EDGE - the same but edges
//            CV_WEIGHTED_ALL - the same but both edges and vertices
//      weight_vtx - optional vector of floats, with size = graph->total.
//            If weight_type is either CV_WEIGHTED_VTX or CV_WEIGHTED_ALL
//            weights of vertices must be provided.  If weight_vtx not zero
//            these weights considered to be here, otherwise function assumes
//            that vertices of graph are inherited from CvGraphWeightedVtx.
//      weight_edge - optional matrix of floats, of width and height = graph->total.
//            If weight_type is either CV_WEIGHTED_EDGE or CV_WEIGHTED_ALL
//            weights of edges ought to be supplied.  If weight_edge is not zero
//            function finds them here, otherwise function expects
//            edges of graph to be inherited from CvGraphWeightedEdge.
//            Note that in case of CV_WEIGHTED_EDGE or CV_WEIGHTED_ALL
//            nonzero is_complementary implies nonzero weight_edge.
//      start_clique - optional sequence of pairwise different ints.  They are indices of
//            vertices that shall be present in the output clique.
//      subgraph_of_ban - optional sequence of (maybe equal) ints.  They are indices of
//            vertices that shall not be present in the output clique.
//      clique_weight_ptr - optional output parameter.  Weight of found clique stored here.
//      num_generations - optional number of generations in evolutionary part of algorithm,
//            zero forces to return first found clique.
//      quality - optional parameter determining degree of required quality/speed tradeoff.
//            Must be in the range from 0 to 9.
//            0 is fast and dirty, 9 is slow but hopefully yields good clique.
//    Returns:
//      sequence of pairwise different ints.
//      These are indices of vertices that form found clique.
//    Notes:
//      in cases of CV_WEIGHTED_EDGE and CV_WEIGHTED_ALL weights should be nonnegative.
//      start_clique has a priority over subgraph_of_ban.
//F*/
/*CVAPI(CvSeq*) cvFindCliqueEx( CvGraph *graph, CvMemStorage *storage,
                                 int is_complementary CV_DEFAULT(0),
                                 CvGraphWeightType weight_type CV_DEFAULT(CV_NOT_WEIGHTED),
                                 CvVect32f weight_vtx CV_DEFAULT(0),
                                 CvMatr32f weight_edge CV_DEFAULT(0),
                                 CvSeq *start_clique CV_DEFAULT(0),
                                 CvSeq *subgraph_of_ban CV_DEFAULT(0),
                                 float *clique_weight_ptr CV_DEFAULT(0),
                                 int num_generations CV_DEFAULT(3),
                                 int quality CV_DEFAULT(2) );*/


#define CV_UNDEF_SC_PARAM         12345 //default value of parameters

#define CV_IDP_BIRCHFIELD_PARAM1  25
#define CV_IDP_BIRCHFIELD_PARAM2  5
#define CV_IDP_BIRCHFIELD_PARAM3  12
#define CV_IDP_BIRCHFIELD_PARAM4  15
#define CV_IDP_BIRCHFIELD_PARAM5  25


#define  CV_DISPARITY_BIRCHFIELD  0


/*F///////////////////////////////////////////////////////////////////////////
//
//    Name:    cvFindStereoCorrespondence
//    Purpose: find stereo correspondence on stereo-pair
//    Context:
//    Parameters:
//      leftImage - left image of stereo-pair (format 8uC1).
//      rightImage - right image of stereo-pair (format 8uC1).
//   mode - mode of correspondence retrieval (now CV_DISPARITY_BIRCHFIELD only)
//      dispImage - destination disparity image
//      maxDisparity - maximal disparity
//      param1, param2, param3, param4, param5 - parameters of algorithm
//    Returns:
//    Notes:
//      Images must be rectified.
//      All images must have format 8uC1.
//F*/
CVAPI(void)
cvFindStereoCorrespondence(
                   const  CvArr* leftImage, const  CvArr* rightImage,
                   int     mode,
                   CvArr*  dispImage,
                   int     maxDisparity,
                   double  param1 CV_DEFAULT(CV_UNDEF_SC_PARAM),
                   double  param2 CV_DEFAULT(CV_UNDEF_SC_PARAM),
                   double  param3 CV_DEFAULT(CV_UNDEF_SC_PARAM),
                   double  param4 CV_DEFAULT(CV_UNDEF_SC_PARAM),
                   double  param5 CV_DEFAULT(CV_UNDEF_SC_PARAM) );

/*****************************************************************************************/
/************ Epiline functions *******************/



typedef struct CvStereoLineCoeff
{
    double Xcoef;
    double XcoefA;
    double XcoefB;
    double XcoefAB;

    double Ycoef;
    double YcoefA;
    double YcoefB;
    double YcoefAB;

    double Zcoef;
    double ZcoefA;
    double ZcoefB;
    double ZcoefAB;
}CvStereoLineCoeff;


typedef struct CvCamera
{
    float   imgSize[2]; /* size of the camera view, used during calibration */
    float   matrix[9]; /* intinsic camera parameters:  [ fx 0 cx; 0 fy cy; 0 0 1 ] */
    float   distortion[4]; /* distortion coefficients - two coefficients for radial distortion
                              and another two for tangential: [ k1 k2 p1 p2 ] */
    float   rotMatr[9];
    float   transVect[3]; /* rotation matrix and transition vector relatively
                             to some reference point in the space. */
} CvCamera;

typedef struct CvStereoCamera
{
    CvCamera* camera[2]; /* two individual camera parameters */
    float fundMatr[9]; /* fundamental matrix */

    /* New part for stereo */
    CvPoint3D32f epipole[2];
    CvPoint2D32f quad[2][4]; /* coordinates of destination quadrangle after
                                epipolar geometry rectification */
    double coeffs[2][3][3];/* coefficients for transformation */
    CvPoint2D32f border[2][4];
    CvSize warpSize;
    CvStereoLineCoeff* lineCoeffs;
    int needSwapCameras;/* flag set to 1 if need to swap cameras for good reconstruction */
    float rotMatrix[9];
    float transVector[3];
} CvStereoCamera;


typedef struct CvContourOrientation
{
    float egvals[2];
    float egvects[4];

    float max, min; // minimum and maximum projections
    int imax, imin;
} CvContourOrientation;

#define CV_CAMERA_TO_WARP 1
#define CV_WARP_TO_CAMERA 2

CVAPI(int) icvConvertWarpCoordinates(double coeffs[3][3],
                                CvPoint2D32f* cameraPoint,
                                CvPoint2D32f* warpPoint,
                                int direction);

CVAPI(int) icvGetSymPoint3D(  CvPoint3D64f pointCorner,
                            CvPoint3D64f point1,
                            CvPoint3D64f point2,
                            CvPoint3D64f *pointSym2);

CVAPI(void) icvGetPieceLength3D(CvPoint3D64f point1,CvPoint3D64f point2,double* dist);

CVAPI(int) icvCompute3DPoint(    double alpha,double betta,
                            CvStereoLineCoeff* coeffs,
                            CvPoint3D64f* point);

CVAPI(int) icvCreateConvertMatrVect( double*     rotMatr1,
                                double*     transVect1,
                                double*     rotMatr2,
                                double*     transVect2,
                                double*     convRotMatr,
                                double*     convTransVect);

CVAPI(int) icvConvertPointSystem(CvPoint3D64f  M2,
                            CvPoint3D64f* M1,
                            double*     rotMatr,
                            double*     transVect
                            );

CVAPI(int) icvComputeCoeffForStereo(  CvStereoCamera* stereoCamera);

CVAPI(int) icvGetCrossPieceVector(CvPoint2D32f p1_start,CvPoint2D32f p1_end,CvPoint2D32f v2_start,CvPoint2D32f v2_end,CvPoint2D32f *cross);
CVAPI(int) icvGetCrossLineDirect(CvPoint2D32f p1,CvPoint2D32f p2,float a,float b,float c,CvPoint2D32f* cross);
CVAPI(float) icvDefinePointPosition(CvPoint2D32f point1,CvPoint2D32f point2,CvPoint2D32f point);
CVAPI(int) icvStereoCalibration( int numImages,
                            int* nums,
                            CvSize imageSize,
                            CvPoint2D32f* imagePoints1,
                            CvPoint2D32f* imagePoints2,
                            CvPoint3D32f* objectPoints,
                            CvStereoCamera* stereoparams
                           );


CVAPI(int) icvComputeRestStereoParams(CvStereoCamera *stereoparams);

CVAPI(void) cvComputePerspectiveMap( const double coeffs[3][3], CvArr* rectMapX, CvArr* rectMapY );

CVAPI(int) icvComCoeffForLine(   CvPoint2D64f point1,
                            CvPoint2D64f point2,
                            CvPoint2D64f point3,
                            CvPoint2D64f point4,
                            double*    camMatr1,
                            double*    rotMatr1,
                            double*    transVect1,
                            double*    camMatr2,
                            double*    rotMatr2,
                            double*    transVect2,
                            CvStereoLineCoeff*    coeffs,
                            int* needSwapCameras);

CVAPI(int) icvGetDirectionForPoint(  CvPoint2D64f point,
                                double* camMatr,
                                CvPoint3D64f* direct);

CVAPI(int) icvGetCrossLines(CvPoint3D64f point11,CvPoint3D64f point12,
                       CvPoint3D64f point21,CvPoint3D64f point22,
                       CvPoint3D64f* midPoint);

CVAPI(int) icvComputeStereoLineCoeffs(   CvPoint3D64f pointA,
                                    CvPoint3D64f pointB,
                                    CvPoint3D64f pointCam1,
                                    double gamma,
                                    CvStereoLineCoeff*    coeffs);

/*CVAPI(int) icvComputeFundMatrEpipoles ( double* camMatr1,
                                    double*     rotMatr1,
                                    double*     transVect1,
                                    double*     camMatr2,
                                    double*     rotMatr2,
                                    double*     transVect2,
                                    CvPoint2D64f* epipole1,
                                    CvPoint2D64f* epipole2,
                                    double*     fundMatr);*/

CVAPI(int) icvGetAngleLine( CvPoint2D64f startPoint, CvSize imageSize,CvPoint2D64f *point1,CvPoint2D64f *point2);

CVAPI(void) icvGetCoefForPiece(   CvPoint2D64f p_start,CvPoint2D64f p_end,
                        double *a,double *b,double *c,
                        int* result);

/*CVAPI(void) icvGetCommonArea( CvSize imageSize,
                    CvPoint2D64f epipole1,CvPoint2D64f epipole2,
                    double* fundMatr,
                    double* coeff11,double* coeff12,
                    double* coeff21,double* coeff22,
                    int* result);*/

CVAPI(void) icvComputeeInfiniteProject1(double*    rotMatr,
                                     double*    camMatr1,
                                     double*    camMatr2,
                                     CvPoint2D32f point1,
                                     CvPoint2D32f *point2);

CVAPI(void) icvComputeeInfiniteProject2(double*    rotMatr,
                                     double*    camMatr1,
                                     double*    camMatr2,
                                     CvPoint2D32f* point1,
                                     CvPoint2D32f point2);

CVAPI(void) icvGetCrossDirectDirect(  double* direct1,double* direct2,
                            CvPoint2D64f *cross,int* result);

CVAPI(void) icvGetCrossPieceDirect(   CvPoint2D64f p_start,CvPoint2D64f p_end,
                            double a,double b,double c,
                            CvPoint2D64f *cross,int* result);

CVAPI(void) icvGetCrossPiecePiece( CvPoint2D64f p1_start,CvPoint2D64f p1_end,
                            CvPoint2D64f p2_start,CvPoint2D64f p2_end,
                            CvPoint2D64f* cross,
                            int* result);

CVAPI(void) icvGetPieceLength(CvPoint2D64f point1,CvPoint2D64f point2,double* dist);

CVAPI(void) icvGetCrossRectDirect(    CvSize imageSize,
                            double a,double b,double c,
                            CvPoint2D64f *start,CvPoint2D64f *end,
                            int* result);

CVAPI(void) icvProjectPointToImage(   CvPoint3D64f point,
                            double* camMatr,double* rotMatr,double* transVect,
                            CvPoint2D64f* projPoint);

CVAPI(void) icvGetQuadsTransform( CvSize        imageSize,
                        double*     camMatr1,
                        double*     rotMatr1,
                        double*     transVect1,
                        double*     camMatr2,
                        double*     rotMatr2,
                        double*     transVect2,
                        CvSize*       warpSize,
                        double quad1[4][2],
                        double quad2[4][2],
                        double*     fundMatr,
                        CvPoint3D64f* epipole1,
                        CvPoint3D64f* epipole2
                        );

CVAPI(void) icvGetQuadsTransformStruct(  CvStereoCamera* stereoCamera);

CVAPI(void) icvComputeStereoParamsForCameras(CvStereoCamera* stereoCamera);

CVAPI(void) icvGetCutPiece(   double* areaLineCoef1,double* areaLineCoef2,
                    CvPoint2D64f epipole,
                    CvSize imageSize,
                    CvPoint2D64f* point11,CvPoint2D64f* point12,
                    CvPoint2D64f* point21,CvPoint2D64f* point22,
                    int* result);

CVAPI(void) icvGetMiddleAnglePoint(   CvPoint2D64f basePoint,
                            CvPoint2D64f point1,CvPoint2D64f point2,
                            CvPoint2D64f* midPoint);

CVAPI(void) icvGetNormalDirect(double* direct,CvPoint2D64f point,double* normDirect);

CVAPI(double) icvGetVect(CvPoint2D64f basePoint,CvPoint2D64f point1,CvPoint2D64f point2);

CVAPI(void) icvProjectPointToDirect(  CvPoint2D64f point,double* lineCoeff,
                            CvPoint2D64f* projectPoint);

CVAPI(void) icvGetDistanceFromPointToDirect( CvPoint2D64f point,double* lineCoef,double*dist);

CVAPI(IplImage*) icvCreateIsometricImage( IplImage* src, IplImage* dst,
                              int desired_depth, int desired_num_channels );

CVAPI(void) cvDeInterlace( const CvArr* frame, CvArr* fieldEven, CvArr* fieldOdd );

/*CVAPI(int) icvSelectBestRt(           int           numImages,
                                    int*          numPoints,
                                    CvSize        imageSize,
                                    CvPoint2D32f* imagePoints1,
                                    CvPoint2D32f* imagePoints2,
                                    CvPoint3D32f* objectPoints,

                                    CvMatr32f     cameraMatrix1,
                                    CvVect32f     distortion1,
                                    CvMatr32f     rotMatrs1,
                                    CvVect32f     transVects1,

                                    CvMatr32f     cameraMatrix2,
                                    CvVect32f     distortion2,
                                    CvMatr32f     rotMatrs2,
                                    CvVect32f     transVects2,

                                    CvMatr32f     bestRotMatr,
                                    CvVect32f     bestTransVect
                                    );*/


/****************************************************************************************\
*                                     Contour Tree                                       *
\****************************************************************************************/

/* 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;

/* Builds hierarhical representation of a contour */
CVAPI(CvContourTree*)  cvCreateContourTree( const CvSeq* contour,
                                            CvMemStorage* storage,
                                            double threshold );

/* Reconstruct (completelly or partially) contour a from contour tree */
CVAPI(CvSeq*)  cvContourFromContourTree( const CvContourTree* tree,
                                         CvMemStorage* storage,
                                         CvTermCriteria criteria );

/* Compares two contour trees */
enum { CV_CONTOUR_TREES_MATCH_I1 = 1 };

CVAPI(double)  cvMatchContourTrees( const CvContourTree* tree1,
                                    const CvContourTree* tree2,
                                    int method, double threshold );

/****************************************************************************************\
*                                   Contour Morphing                                     *
\****************************************************************************************/

/* finds correspondence between two contours */
CvSeq* cvCalcContoursCorrespondence( const CvSeq* contour1,
                                     const CvSeq* contour2,
                                     CvMemStorage* storage);

/* morphs contours using the pre-calculated correspondence:
   alpha=0 ~ contour1, alpha=1 ~ contour2 */
CvSeq* cvMorphContours( const CvSeq* contour1, const CvSeq* contour2,
                        CvSeq* corr, double alpha,
                        CvMemStorage* storage );


/****************************************************************************************\
*                                   Active Contours                                      *
\****************************************************************************************/

#define  CV_VALUE  1
#define  CV_ARRAY  2
/* Updates active contour in order to minimize its cummulative
   (internal and external) energy. */
CVAPI(void)  cvSnakeImage( const IplImage* image, CvPoint* points,
                           int  length, float* alpha,
                           float* beta, float* gamma,
                           int coeff_usage, CvSize  win,
                           CvTermCriteria criteria, int calc_gradient CV_DEFAULT(1));

/****************************************************************************************\
*                                    Texture Descriptors                                 *
\****************************************************************************************/

#define CV_GLCM_OPTIMIZATION_NONE                   -2
#define CV_GLCM_OPTIMIZATION_LUT                    -1
#define CV_GLCM_OPTIMIZATION_HISTOGRAM              0

#define CV_GLCMDESC_OPTIMIZATION_ALLOWDOUBLENEST    10
#define CV_GLCMDESC_OPTIMIZATION_ALLOWTRIPLENEST    11
#define CV_GLCMDESC_OPTIMIZATION_HISTOGRAM          4

#define CV_GLCMDESC_ENTROPY                         0
#define CV_GLCMDESC_ENERGY                          1
#define CV_GLCMDESC_HOMOGENITY                      2
#define CV_GLCMDESC_CONTRAST                        3
#define CV_GLCMDESC_CLUSTERTENDENCY                 4
#define CV_GLCMDESC_CLUSTERSHADE                    5
#define CV_GLCMDESC_CORRELATION                     6
#define CV_GLCMDESC_CORRELATIONINFO1                7
#define CV_GLCMDESC_CORRELATIONINFO2                8
#define CV_GLCMDESC_MAXIMUMPROBABILITY              9

#define CV_GLCM_ALL                                 0
#define CV_GLCM_GLCM                                1
#define CV_GLCM_DESC                                2

typedef struct CvGLCM CvGLCM;

CVAPI(CvGLCM*) cvCreateGLCM( const IplImage* srcImage,
                                int stepMagnitude,
                                const int* stepDirections CV_DEFAULT(0),
                                int numStepDirections CV_DEFAULT(0),
                                int optimizationType CV_DEFAULT(CV_GLCM_OPTIMIZATION_NONE));

CVAPI(void) cvReleaseGLCM( CvGLCM** GLCM, int flag CV_DEFAULT(CV_GLCM_ALL));

CVAPI(void) cvCreateGLCMDescriptors( CvGLCM* destGLCM,
                                        int descriptorOptimizationType
                                        CV_DEFAULT(CV_GLCMDESC_OPTIMIZATION_ALLOWDOUBLENEST));

CVAPI(double) cvGetGLCMDescriptor( CvGLCM* GLCM, int step, int descriptor );

CVAPI(void) cvGetGLCMDescriptorStatistics( CvGLCM* GLCM, int descriptor,
                                              double* average, double* standardDeviation );

CVAPI(IplImage*) cvCreateGLCMImage( CvGLCM* GLCM, int step );

/****************************************************************************************\
*                                  Face eyes&mouth tracking                              *
\****************************************************************************************/


typedef struct CvFaceTracker CvFaceTracker;

#define CV_NUM_FACE_ELEMENTS    3
enum CV_FACE_ELEMENTS
{
    CV_FACE_MOUTH = 0,
    CV_FACE_LEFT_EYE = 1,
    CV_FACE_RIGHT_EYE = 2
};

CVAPI(CvFaceTracker*) cvInitFaceTracker(CvFaceTracker* pFaceTracking, const IplImage* imgGray,
                                                CvRect* pRects, int nRects);
CVAPI(int) cvTrackFace( CvFaceTracker* pFaceTracker, IplImage* imgGray,
                              CvRect* pRects, int nRects,
                              CvPoint* ptRotate, double* dbAngleRotate);
CVAPI(void) cvReleaseFaceTracker(CvFaceTracker** ppFaceTracker);


typedef struct CvFace
{
    CvRect MouthRect;
    CvRect LeftEyeRect;
    CvRect RightEyeRect;
} CvFaceData;

CvSeq * cvFindFace(IplImage * Image,CvMemStorage* storage);
CvSeq * cvPostBoostingFindFace(IplImage * Image,CvMemStorage* storage);


/****************************************************************************************\
*                                         3D Tracker                                     *
\****************************************************************************************/

typedef unsigned char CvBool;

typedef struct Cv3dTracker2dTrackedObject
{
    int id;
    CvPoint2D32f p; // pgruebele: So we do not loose precision, this needs to be float
} Cv3dTracker2dTrackedObject;

CV_INLINE Cv3dTracker2dTrackedObject cv3dTracker2dTrackedObject(int id, CvPoint2D32f p)
{
    Cv3dTracker2dTrackedObject r;
    r.id = id;
    r.p = p;
    return r;
}

typedef struct Cv3dTrackerTrackedObject
{
    int id;
    CvPoint3D32f p;             // location of the tracked object
} Cv3dTrackerTrackedObject;

CV_INLINE Cv3dTrackerTrackedObject cv3dTrackerTrackedObject(int id, CvPoint3D32f p)
{
    Cv3dTrackerTrackedObject r;
    r.id = id;
    r.p = p;
    return r;
}

typedef struct Cv3dTrackerCameraInfo
{
    CvBool valid;
    float mat[4][4];              /* maps camera coordinates to world coordinates */
    CvPoint2D32f principal_point; /* copied from intrinsics so this structure */
                                  /* has all the info we need */
} Cv3dTrackerCameraInfo;

typedef struct Cv3dTrackerCameraIntrinsics
{
    CvPoint2D32f principal_point;
    float focal_length[2];
    float distortion[4];
} Cv3dTrackerCameraIntrinsics;

CVAPI(CvBool) cv3dTrackerCalibrateCameras(int num_cameras,
                     const Cv3dTrackerCameraIntrinsics camera_intrinsics[], /* size is num_cameras */
                     CvSize etalon_size,
                     float square_size,
                     IplImage *samples[],                                   /* size is num_cameras */
                     Cv3dTrackerCameraInfo camera_info[]);                  /* size is num_cameras */

CVAPI(int)  cv3dTrackerLocateObjects(int num_cameras, int num_objects,
                   const Cv3dTrackerCameraInfo camera_info[],        /* size is num_cameras */
                   const Cv3dTracker2dTrackedObject tracking_info[], /* size is num_objects*num_cameras */
                   Cv3dTrackerTrackedObject tracked_objects[]);      /* size is num_objects */
/****************************************************************************************
 tracking_info is a rectangular array; one row per camera, num_objects elements per row.
 The id field of any unused slots must be -1. Ids need not be ordered or consecutive. On
 completion, the return value is the number of objects located; i.e., the number of objects
 visible by more than one camera. The id field of any unused slots in tracked objects is
 set to -1.
****************************************************************************************/


/****************************************************************************************\
*                           Skeletons and Linear-Contour Models                          *
\****************************************************************************************/

typedef enum CvLeeParameters
{
    CV_LEE_INT = 0,
    CV_LEE_FLOAT = 1,
    CV_LEE_DOUBLE = 2,
    CV_LEE_AUTO = -1,
    CV_LEE_ERODE = 0,
    CV_LEE_ZOOM = 1,
    CV_LEE_NON = 2
} CvLeeParameters;

#define CV_NEXT_VORONOISITE2D( SITE ) ((SITE)->edge[0]->site[((SITE)->edge[0]->site[0] == (SITE))])
#define CV_PREV_VORONOISITE2D( SITE ) ((SITE)->edge[1]->site[((SITE)->edge[1]->site[0] == (SITE))])
#define CV_FIRST_VORONOIEDGE2D( SITE ) ((SITE)->edge[0])
#define CV_LAST_VORONOIEDGE2D( SITE ) ((SITE)->edge[1])
#define CV_NEXT_VORONOIEDGE2D( EDGE, SITE ) ((EDGE)->next[(EDGE)->site[0] != (SITE)])
#define CV_PREV_VORONOIEDGE2D( EDGE, SITE ) ((EDGE)->next[2 + ((EDGE)->site[0] != (SITE))])
#define CV_VORONOIEDGE2D_BEGINNODE( EDGE, SITE ) ((EDGE)->node[((EDGE)->site[0] != (SITE))])
#define CV_VORONOIEDGE2D_ENDNODE( EDGE, SITE ) ((EDGE)->node[((EDGE)->site[0] == (SITE))])
#define CV_TWIN_VORONOISITE2D( SITE, EDGE ) ( (EDGE)->site[((EDGE)->site[0] == (SITE))])

#define CV_VORONOISITE2D_FIELDS()    \
    struct CvVoronoiNode2D *node[2]; \
    struct CvVoronoiEdge2D *edge[2];

typedef struct CvVoronoiSite2D
{
    CV_VORONOISITE2D_FIELDS()
    struct CvVoronoiSite2D *next[2];
} CvVoronoiSite2D;

#define CV_VORONOIEDGE2D_FIELDS()    \
    struct CvVoronoiNode2D *node[2]; \
    struct CvVoronoiSite2D *site[2]; \
    struct CvVoronoiEdge2D *next[4];

typedef struct CvVoronoiEdge2D
{
    CV_VORONOIEDGE2D_FIELDS()
} CvVoronoiEdge2D;

#define CV_VORONOINODE2D_FIELDS()       \
    CV_SET_ELEM_FIELDS(CvVoronoiNode2D) \
    CvPoint2D32f pt;                    \
    float radius;

typedef struct CvVoronoiNode2D
{
    CV_VORONOINODE2D_FIELDS()
} CvVoronoiNode2D;

#define CV_VORONOIDIAGRAM2D_FIELDS() \
    CV_GRAPH_FIELDS()                \
    CvSet *sites;

typedef struct CvVoronoiDiagram2D
{
    CV_VORONOIDIAGRAM2D_FIELDS()
} CvVoronoiDiagram2D;

/* Computes Voronoi Diagram for given polygons with holes */
CVAPI(int)  cvVoronoiDiagramFromContour(CvSeq* ContourSeq,
                                           CvVoronoiDiagram2D** VoronoiDiagram,
                                           CvMemStorage* VoronoiStorage,
                                           CvLeeParameters contour_type CV_DEFAULT(CV_LEE_INT),
                                           int contour_orientation CV_DEFAULT(-1),
                                           int attempt_number CV_DEFAULT(10));

/* Computes Voronoi Diagram for domains in given image */
CVAPI(int)  cvVoronoiDiagramFromImage(IplImage* pImage,
                                         CvSeq** ContourSeq,
                                         CvVoronoiDiagram2D** VoronoiDiagram,
                                         CvMemStorage* VoronoiStorage,
                                         CvLeeParameters regularization_method CV_DEFAULT(CV_LEE_NON),
                                         float approx_precision CV_DEFAULT(CV_LEE_AUTO));

/* Deallocates the storage */
CVAPI(void) cvReleaseVoronoiStorage(CvVoronoiDiagram2D* VoronoiDiagram,
                                          CvMemStorage** pVoronoiStorage);

/*********************** Linear-Contour Model ****************************/

struct CvLCMEdge;
struct CvLCMNode;

typedef struct CvLCMEdge
{
    CV_GRAPH_EDGE_FIELDS()
    CvSeq* chain;
    float width;
    int index1;
    int index2;
} CvLCMEdge;

typedef struct CvLCMNode
{
    CV_GRAPH_VERTEX_FIELDS()
    CvContour* contour;
} CvLCMNode;


/* Computes hybrid model from Voronoi Diagram */
CVAPI(CvGraph*) cvLinearContorModelFromVoronoiDiagram(CvVoronoiDiagram2D* VoronoiDiagram,
                                                         float maxWidth);

/* Releases hybrid model storage */
CVAPI(int) cvReleaseLinearContorModelStorage(CvGraph** Graph);


/* two stereo-related functions */

CVAPI(void) cvInitPerspectiveTransform( CvSize size, const CvPoint2D32f vertex[4], double matrix[3][3],
                                              CvArr* rectMap );

/*CVAPI(void) cvInitStereoRectification( CvStereoCamera* params,
                                             CvArr* rectMap1, CvArr* rectMap2,
                                             int do_undistortion );*/

/*************************** View Morphing Functions ************************/

typedef struct CvMatrix3
{
    float m[3][3];
} CvMatrix3;

/* The order of the function corresponds to the order they should appear in
   the view morphing pipeline */

/* Finds ending points of scanlines on left and right images of stereo-pair */
CVAPI(void)  cvMakeScanlines( const CvMatrix3* matrix, CvSize  img_size,
                              int*  scanlines1, int*  scanlines2,
                              int*  lengths1, int*  lengths2,
                              int*  line_count );

/* Grab pixel values from scanlines and stores them sequentially
   (some sort of perspective image transform) */
CVAPI(void)  cvPreWarpImage( int       line_count,
                             IplImage* img,
                             uchar*    dst,
                             int*      dst_nums,
                             int*      scanlines);

/* Approximate each grabbed scanline by a sequence of runs
   (lossy run-length compression) */
CVAPI(void)  cvFindRuns( int    line_count,
                         uchar* prewarp1,
                         uchar* prewarp2,
                         int*   line_lengths1,
                         int*   line_lengths2,
                         int*   runs1,
                         int*   runs2,
                         int*   num_runs1,
                         int*   num_runs2);

/* Compares two sets of compressed scanlines */
CVAPI(void)  cvDynamicCorrespondMulti( int  line_count,
                                       int* first,
                                       int* first_runs,
                                       int* second,
                                       int* second_runs,
                                       int* first_corr,
                                       int* second_corr);

/* Finds scanline ending coordinates for some intermediate "virtual" camera position */
CVAPI(void)  cvMakeAlphaScanlines( int*  scanlines1,
                                   int*  scanlines2,
                                   int*  scanlinesA,
                                   int*  lengths,
                                   int   line_count,
                                   float alpha);

/* Blends data of the left and right image scanlines to get
   pixel values of "virtual" image scanlines */
CVAPI(void)  cvMorphEpilinesMulti( int    line_count,
                                   uchar* first_pix,
                                   int*   first_num,
                                   uchar* second_pix,
                                   int*   second_num,
                                   uchar* dst_pix,
                                   int*   dst_num,
                                   float  alpha,
                                   int*   first,
                                   int*   first_runs,
                                   int*   second,
                                   int*   second_runs,
                                   int*   first_corr,
                                   int*   second_corr);

/* Does reverse warping of the morphing result to make
   it fill the destination image rectangle */
CVAPI(void)  cvPostWarpImage( int       line_count,
                              uchar*    src,
                              int*      src_nums,
                              IplImage* img,
                              int*      scanlines);

/* Deletes Moire (missed pixels that appear due to discretization) */
CVAPI(void)  cvDeleteMoire( IplImage*  img );


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;

/* Creates ConDensation filter state */
CVAPI(CvConDensation*)  cvCreateConDensation( int dynam_params,
                                             int measure_params,
                                             int sample_count );

/* Releases ConDensation filter state */
CVAPI(void)  cvReleaseConDensation( CvConDensation** condens );

/* Updates ConDensation filter by time (predict future state of the system) */
CVAPI(void)  cvConDensUpdateByTime( CvConDensation* condens);

/* Initializes ConDensation filter samples  */
CVAPI(void)  cvConDensInitSampleSet( CvConDensation* condens, CvMat* lower_bound, CvMat* upper_bound );

CV_INLINE int iplWidth( const IplImage* img )
{
    return !img ? 0 : !img->roi ? img->width : img->roi->width;
}

CV_INLINE int iplHeight( const IplImage* img )
{
    return !img ? 0 : !img->roi ? img->height : img->roi->height;
}

#ifdef __cplusplus
}
#endif

#ifdef __cplusplus

/****************************************************************************************\
*                                   Calibration engine                                   *
\****************************************************************************************/

typedef enum CvCalibEtalonType
{
    CV_CALIB_ETALON_USER = -1,
    CV_CALIB_ETALON_CHESSBOARD = 0,
    CV_CALIB_ETALON_CHECKERBOARD = CV_CALIB_ETALON_CHESSBOARD
}
CvCalibEtalonType;

class CV_EXPORTS CvCalibFilter
{
public:
    /* Constructor & destructor */
    CvCalibFilter();
    virtual ~CvCalibFilter();

    /* Sets etalon type - one for all cameras.
       etalonParams is used in case of pre-defined etalons (such as chessboard).
       Number of elements in etalonParams is determined by etalonType.
       E.g., if etalon type is CV_ETALON_TYPE_CHESSBOARD then:
         etalonParams[0] is number of squares per one side of etalon
         etalonParams[1] is number of squares per another side of etalon
         etalonParams[2] is linear size of squares in the board in arbitrary units.
       pointCount & points are used in case of
       CV_CALIB_ETALON_USER (user-defined) etalon. */
    virtual bool
        SetEtalon( CvCalibEtalonType etalonType, double* etalonParams,
                   int pointCount = 0, CvPoint2D32f* points = 0 );

    /* Retrieves etalon parameters/or and points */
    virtual CvCalibEtalonType
        GetEtalon( int* paramCount = 0, const double** etalonParams = 0,
                   int* pointCount = 0, const CvPoint2D32f** etalonPoints = 0 ) const;

    /* Sets number of cameras calibrated simultaneously. It is equal to 1 initially */
    virtual void SetCameraCount( int cameraCount );

    /* Retrieves number of cameras */
    int GetCameraCount() const { return cameraCount; }

    /* Starts cameras calibration */
    virtual bool SetFrames( int totalFrames );

    /* Stops cameras calibration */
    virtual void Stop( bool calibrate = false );

    /* Retrieves number of cameras */
    bool IsCalibrated() const { return isCalibrated; }

    /* Feeds another serie of snapshots (one per each camera) to filter.
       Etalon points on these images are found automatically.
       If the function can't locate points, it returns false */
    virtual bool FindEtalon( IplImage** imgs );

    /* The same but takes matrices */
    virtual bool FindEtalon( CvMat** imgs );

    /* Lower-level function for feeding filter with already found etalon points.
       Array of point arrays for each camera is passed. */
    virtual bool Push( const CvPoint2D32f** points = 0 );

    /* Returns total number of accepted frames and, optionally,
       total number of frames to collect */
    virtual int GetFrameCount( int* framesTotal = 0 ) const;

    /* Retrieves camera parameters for specified camera.
       If camera is not calibrated the function returns 0 */
    virtual const CvCamera* GetCameraParams( int idx = 0 ) const;

    virtual const CvStereoCamera* GetStereoParams() const;

    /* Sets camera parameters for all cameras */
    virtual bool SetCameraParams( CvCamera* params );

    /* Saves all camera parameters to file */
    virtual bool SaveCameraParams( const char* filename );

    /* Loads all camera parameters from file */
    virtual bool LoadCameraParams( const char* filename );

    /* Undistorts images using camera parameters. Some of src pointers can be NULL. */
    virtual bool Undistort( IplImage** src, IplImage** dst );

    /* Undistorts images using camera parameters. Some of src pointers can be NULL. */
    virtual bool Undistort( CvMat** src, CvMat** dst );

    /* Returns array of etalon points detected/partally detected
       on the latest frame for idx-th camera */
    virtual bool GetLatestPoints( int idx, CvPoint2D32f** pts,
                                                  int* count, bool* found );

    /* Draw the latest detected/partially detected etalon */
    virtual void DrawPoints( IplImage** dst );

    /* Draw the latest detected/partially detected etalon */
    virtual void DrawPoints( CvMat** dst );

    virtual bool Rectify( IplImage** srcarr, IplImage** dstarr );
    virtual bool Rectify( CvMat** srcarr, CvMat** dstarr );

protected:

    enum { MAX_CAMERAS = 3 };

    /* etalon data */
    CvCalibEtalonType  etalonType;
    int     etalonParamCount;
    double* etalonParams;
    int     etalonPointCount;
    CvPoint2D32f* etalonPoints;
    CvSize  imgSize;
    CvMat*  grayImg;
    CvMat*  tempImg;
    CvMemStorage* storage;

    /* camera data */
    int     cameraCount;
    CvCamera cameraParams[MAX_CAMERAS];
    CvStereoCamera stereo;
    CvPoint2D32f* points[MAX_CAMERAS];
    CvMat*  undistMap[MAX_CAMERAS][2];
    CvMat*  undistImg;
    int     latestCounts[MAX_CAMERAS];
    CvPoint2D32f* latestPoints[MAX_CAMERAS];
    CvMat*  rectMap[MAX_CAMERAS][2];

    /* Added by Valery */
    //CvStereoCamera stereoParams;

    int     maxPoints;
    int     framesTotal;
    int     framesAccepted;
    bool    isCalibrated;
};

#include <iosfwd>
#include <limits>

class CV_EXPORTS CvImage
{
public:
    CvImage() : image(0), refcount(0) {}
    CvImage( CvSize _size, int _depth, int _channels )
    {
        image = cvCreateImage( _size, _depth, _channels );
        refcount = image ? new int(1) : 0;
    }

    CvImage( IplImage* img ) : image(img)
    {
        refcount = image ? new int(1) : 0;
    }

    CvImage( const CvImage& img ) : image(img.image), refcount(img.refcount)
    {
        if( refcount ) ++(*refcount);
    }

    CvImage( const char* filename, const char* imgname=0, int color=-1 ) : image(0), refcount(0)
    { load( filename, imgname, color ); }

    CvImage( CvFileStorage* fs, const char* mapname, const char* imgname ) : image(0), refcount(0)
    { read( fs, mapname, imgname ); }

    CvImage( CvFileStorage* fs, const char* seqname, int idx ) : image(0), refcount(0)
    { read( fs, seqname, idx ); }

    ~CvImage()
    {
        if( refcount && !(--*refcount) )
        {
            cvReleaseImage( &image );
            delete refcount;
        }
    }

    CvImage clone() { return CvImage(image ? cvCloneImage(image) : 0); }

    void create( CvSize _size, int _depth, int _channels )
    {
        if( !image || !refcount ||
           image->width != _size.width || image->height != _size.height ||
           image->depth != _depth || image->nChannels != _channels )
            attach( cvCreateImage( _size, _depth, _channels ));
    }

    void release() { detach(); }
    void clear() { detach(); }

    void attach( IplImage* img, bool use_refcount=true )
    {
        if( refcount && --*refcount == 0 )
        {
            cvReleaseImage( &image );
            delete refcount;
        }
        image = img;
        refcount = use_refcount && image ? new int(1) : 0;
    }

    void detach()
    {
        if( refcount && --*refcount == 0 )
        {
            cvReleaseImage( &image );
            delete refcount;
        }
        image = 0;
        refcount = 0;
    }

    bool load( const char* filename, const char* imgname=0, int color=-1 );
    bool read( CvFileStorage* fs, const char* mapname, const char* imgname );
    bool read( CvFileStorage* fs, const char* seqname, int idx );
    void save( const char* filename, const char* imgname, const int* params=0 );
    void write( CvFileStorage* fs, const char* imgname );

    void show( const char* window_name );
    bool is_valid() { return image != 0; }

    int width() const { return image ? image->width : 0; }
    int height() const { return image ? image->height : 0; }

    CvSize size() const { return image ? cvSize(image->width, image->height) : cvSize(0,0); }

    CvSize roi_size() const
    {
        return !image ? cvSize(0,0) :
        !image->roi ? cvSize(image->width,image->height) :
        cvSize(image->roi->width, image->roi->height);
    }

    CvRect roi() const
    {
        return !image ? cvRect(0,0,0,0) :
        !image->roi ? cvRect(0,0,image->width,image->height) :
        cvRect(image->roi->xOffset,image->roi->yOffset,
               image->roi->width,image->roi->height);
    }

    int coi() const { return !image || !image->roi ? 0 : image->roi->coi; }

    void set_roi(CvRect _roi) { cvSetImageROI(image,_roi); }
    void reset_roi() { cvResetImageROI(image); }
    void set_coi(int _coi) { cvSetImageCOI(image,_coi); }
    int depth() const { return image ? image->depth : 0; }
    int channels() const { return image ? image->nChannels : 0; }
    int pix_size() const { return image ? ((image->depth & 255)>>3)*image->nChannels : 0; }

    uchar* data() { return image ? (uchar*)image->imageData : 0; }
    const uchar* data() const { return image ? (const uchar*)image->imageData : 0; }
    int step() const { return image ? image->widthStep : 0; }
    int origin() const { return image ? image->origin : 0; }

    uchar* roi_row(int y)
    {
        assert(0<=y);
        assert(!image ?
               1 : image->roi ?
               y<image->roi->height : y<image->height);

        return !image ? 0 :
        !image->roi ?
        (uchar*)(image->imageData + y*image->widthStep) :
        (uchar*)(image->imageData + (y+image->roi->yOffset)*image->widthStep +
                 image->roi->xOffset*((image->depth & 255)>>3)*image->nChannels);
    }

    const uchar* roi_row(int y) const
    {
        assert(0<=y);
        assert(!image ?
               1 : image->roi ?
               y<image->roi->height : y<image->height);

        return !image ? 0 :
        !image->roi ?
        (const uchar*)(image->imageData + y*image->widthStep) :
        (const uchar*)(image->imageData + (y+image->roi->yOffset)*image->widthStep +
                       image->roi->xOffset*((image->depth & 255)>>3)*image->nChannels);
    }

    operator const IplImage* () const { return image; }
    operator IplImage* () { return image; }

    CvImage& operator = (const CvImage& img)
    {
        if( img.refcount )
            ++*img.refcount;
        if( refcount && !(--*refcount) )
            cvReleaseImage( &image );
        image=img.image;
        refcount=img.refcount;
        return *this;
    }

protected:
    IplImage* image;
    int* refcount;
};


class CV_EXPORTS CvMatrix
{
public:
    CvMatrix() : matrix(0) {}
    CvMatrix( int _rows, int _cols, int _type )
    { matrix = cvCreateMat( _rows, _cols, _type ); }

    CvMatrix( int _rows, int _cols, int _type, CvMat* hdr,
             void* _data=0, int _step=CV_AUTOSTEP )
    { matrix = cvInitMatHeader( hdr, _rows, _cols, _type, _data, _step ); }

    CvMatrix( int rows, int cols, int type, CvMemStorage* storage, bool alloc_data=true );

    CvMatrix( int _rows, int _cols, int _type, void* _data, int _step=CV_AUTOSTEP )
    { matrix = cvCreateMatHeader( _rows, _cols, _type );
        cvSetData( matrix, _data, _step ); }

    CvMatrix( CvMat* m )
    { matrix = m; }

    CvMatrix( const CvMatrix& m )
    {
        matrix = m.matrix;
        addref();
    }

    CvMatrix( const char* filename, const char* matname=0, int color=-1 ) : matrix(0)
    {  load( filename, matname, color ); }

    CvMatrix( CvFileStorage* fs, const char* mapname, const char* matname ) : matrix(0)
    {  read( fs, mapname, matname ); }

    CvMatrix( CvFileStorage* fs, const char* seqname, int idx ) : matrix(0)
    {  read( fs, seqname, idx ); }

    ~CvMatrix()
    {
        release();
    }

    CvMatrix clone() { return CvMatrix(matrix ? cvCloneMat(matrix) : 0); }

    void set( CvMat* m, bool add_ref )
    {
        release();
        matrix = m;
        if( add_ref )
            addref();
    }

    void create( int _rows, int _cols, int _type )
    {
        if( !matrix || !matrix->refcount ||
           matrix->rows != _rows || matrix->cols != _cols ||
           CV_MAT_TYPE(matrix->type) != _type )
            set( cvCreateMat( _rows, _cols, _type ), false );
    }

    void addref() const
    {
        if( matrix )
        {
            if( matrix->hdr_refcount )
                ++matrix->hdr_refcount;
            else if( matrix->refcount )
                ++*matrix->refcount;
        }
    }

    void release()
    {
        if( matrix )
        {
            if( matrix->hdr_refcount )
            {
                if( --matrix->hdr_refcount == 0 )
                    cvReleaseMat( &matrix );
            }
            else if( matrix->refcount )
            {
                if( --*matrix->refcount == 0 )
                    cvFree( &matrix->refcount );
            }
            matrix = 0;
        }
    }

    void clear()
    {
        release();
    }

    bool load( const char* filename, const char* matname=0, int color=-1 );
    bool read( CvFileStorage* fs, const char* mapname, const char* matname );
    bool read( CvFileStorage* fs, const char* seqname, int idx );
    void save( const char* filename, const char* matname, const int* params=0 );
    void write( CvFileStorage* fs, const char* matname );

    void show( const char* window_name );

    bool is_valid() { return matrix != 0; }

    int rows() const { return matrix ? matrix->rows : 0; }
    int cols() const { return matrix ? matrix->cols : 0; }

    CvSize size() const
    {
        return !matrix ? cvSize(0,0) : cvSize(matrix->rows,matrix->cols);
    }

    int type() const { return matrix ? CV_MAT_TYPE(matrix->type) : 0; }
    int depth() const { return matrix ? CV_MAT_DEPTH(matrix->type) : 0; }
    int channels() const { return matrix ? CV_MAT_CN(matrix->type) : 0; }
    int pix_size() const { return matrix ? CV_ELEM_SIZE(matrix->type) : 0; }

    uchar* data() { return matrix ? matrix->data.ptr : 0; }
    const uchar* data() const { return matrix ? matrix->data.ptr : 0; }
    int step() const { return matrix ? matrix->step : 0; }

    void set_data( void* _data, int _step=CV_AUTOSTEP )
    { cvSetData( matrix, _data, _step ); }

    uchar* row(int i) { return !matrix ? 0 : matrix->data.ptr + i*matrix->step; }
    const uchar* row(int i) const
    { return !matrix ? 0 : matrix->data.ptr + i*matrix->step; }

    operator const CvMat* () const { return matrix; }
    operator CvMat* () { return matrix; }

    CvMatrix& operator = (const CvMatrix& _m)
    {
        _m.addref();
        release();
        matrix = _m.matrix;
        return *this;
    }

protected:
    CvMat* matrix;
};

/****************************************************************************************\
 *                                       CamShiftTracker                                  *
 \****************************************************************************************/

class CV_EXPORTS CvCamShiftTracker
{
public:

    CvCamShiftTracker();
    virtual ~CvCamShiftTracker();

    /**** Characteristics of the object that are calculated by track_object method *****/
    float   get_orientation() const // orientation of the object in degrees
    { return m_box.angle; }
    float   get_length() const // the larger linear size of the object
    { return m_box.size.height; }
    float   get_width() const // the smaller linear size of the object
    { return m_box.size.width; }
    CvPoint2D32f get_center() const // center of the object
    { return m_box.center; }
    CvRect get_window() const // bounding rectangle for the object
    { return m_comp.rect; }

    /*********************** Tracking parameters ************************/
    int     get_threshold() const // thresholding value that applied to back project
    { return m_threshold; }

    int     get_hist_dims( int* dims = 0 ) const // returns number of histogram dimensions and sets
    { return m_hist ? cvGetDims( m_hist->bins, dims ) : 0; }

    int     get_min_ch_val( int channel ) const // get the minimum allowed value of the specified channel
    { return m_min_ch_val[channel]; }

    int     get_max_ch_val( int channel ) const // get the maximum allowed value of the specified channel
    { return m_max_ch_val[channel]; }

    // set initial object rectangle (must be called before initial calculation of the histogram)
    bool    set_window( CvRect window)
    { m_comp.rect = window; return true; }

    bool    set_threshold( int threshold ) // threshold applied to the histogram bins
    { m_threshold = threshold; return true; }

    bool    set_hist_bin_range( int dim, int min_val, int max_val );

    bool    set_hist_dims( int c_dims, int* dims );// set the histogram parameters

    bool    set_min_ch_val( int channel, int val ) // set the minimum allowed value of the specified channel
    { m_min_ch_val[channel] = val; return true; }
    bool    set_max_ch_val( int channel, int val ) // set the maximum allowed value of the specified channel
    { m_max_ch_val[channel] = val; return true; }

    /************************ The processing methods *********************************/
    // update object position
    virtual bool  track_object( const IplImage* cur_frame );

    // update object histogram
    virtual bool  update_histogram( const IplImage* cur_frame );

    // reset histogram
    virtual void  reset_histogram();

    /************************ Retrieving internal data *******************************/
    // get back project image
    virtual IplImage* get_back_project()
    { return m_back_project; }

    float query( int* bin ) const
    { return m_hist ? (float)cvGetRealND(m_hist->bins, bin) : 0.f; }

protected:

    // internal method for color conversion: fills m_color_planes group
    virtual void color_transform( const IplImage* img );

    CvHistogram* m_hist;

    CvBox2D    m_box;
    CvConnectedComp m_comp;

    float      m_hist_ranges_data[CV_MAX_DIM][2];
    float*     m_hist_ranges[CV_MAX_DIM];

    int        m_min_ch_val[CV_MAX_DIM];
    int        m_max_ch_val[CV_MAX_DIM];
    int        m_threshold;

    IplImage*  m_color_planes[CV_MAX_DIM];
    IplImage*  m_back_project;
    IplImage*  m_temp;
    IplImage*  m_mask;
};

/****************************************************************************************\
*                              Expectation - Maximization                                *
\****************************************************************************************/
struct CV_EXPORTS_W_MAP CvEMParams
{
    CvEMParams();
    CvEMParams( int nclusters, int cov_mat_type=cv::EM::COV_MAT_DIAGONAL,
                int start_step=cv::EM::START_AUTO_STEP,
                CvTermCriteria term_crit=cvTermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 100, FLT_EPSILON),
                const CvMat* probs=0, const CvMat* weights=0, const CvMat* means=0, const CvMat** covs=0 );

    CV_PROP_RW int nclusters;
    CV_PROP_RW int cov_mat_type;
    CV_PROP_RW int start_step;
    const CvMat* probs;
    const CvMat* weights;
    const CvMat* means;
    const CvMat** covs;
    CV_PROP_RW CvTermCriteria term_crit;
};


class CV_EXPORTS_W CvEM : public CvStatModel
{
public:
    // Type of covariation matrices
    enum { COV_MAT_SPHERICAL=cv::EM::COV_MAT_SPHERICAL,
           COV_MAT_DIAGONAL =cv::EM::COV_MAT_DIAGONAL,
           COV_MAT_GENERIC  =cv::EM::COV_MAT_GENERIC };

    // The initial step
    enum { START_E_STEP=cv::EM::START_E_STEP,
           START_M_STEP=cv::EM::START_M_STEP,
           START_AUTO_STEP=cv::EM::START_AUTO_STEP };

    CV_WRAP CvEM();
    CvEM( const CvMat* samples, const CvMat* sampleIdx=0,
          CvEMParams params=CvEMParams(), CvMat* labels=0 );

    virtual ~CvEM();

    virtual bool train( const CvMat* samples, const CvMat* sampleIdx=0,
                        CvEMParams params=CvEMParams(), CvMat* labels=0 );

    virtual float predict( const CvMat* sample, CV_OUT CvMat* probs ) const;

    CV_WRAP CvEM( const cv::Mat& samples, const cv::Mat& sampleIdx=cv::Mat(),
                  CvEMParams params=CvEMParams() );

    CV_WRAP virtual bool train( const cv::Mat& samples,
                                const cv::Mat& sampleIdx=cv::Mat(),
                                CvEMParams params=CvEMParams(),
                                CV_OUT cv::Mat* labels=0 );

    CV_WRAP virtual float predict( const cv::Mat& sample, CV_OUT cv::Mat* probs=0 ) const;
    CV_WRAP virtual double calcLikelihood( const cv::Mat &sample ) const;

    CV_WRAP int getNClusters() const;
    CV_WRAP cv::Mat getMeans() const;
    CV_WRAP void getCovs(CV_OUT std::vector<cv::Mat>& covs) const;
    CV_WRAP cv::Mat getWeights() const;
    CV_WRAP cv::Mat getProbs() const;

    CV_WRAP inline double getLikelihood() const { return emObj.isTrained() ? logLikelihood : DBL_MAX; }

    CV_WRAP virtual void clear();

    int get_nclusters() const;
    const CvMat* get_means() const;
    const CvMat** get_covs() const;
    const CvMat* get_weights() const;
    const CvMat* get_probs() const;

    inline double get_log_likelihood() const { return getLikelihood(); }

    virtual void read( CvFileStorage* fs, CvFileNode* node );
    virtual void write( CvFileStorage* fs, const char* name ) const;

protected:
    void set_mat_hdrs();

    cv::EM emObj;
    cv::Mat probs;
    double logLikelihood;

    CvMat meansHdr;
    std::vector<CvMat> covsHdrs;
    std::vector<CvMat*> covsPtrs;
    CvMat weightsHdr;
    CvMat probsHdr;
};

namespace cv
{

typedef CvEMParams EMParams;
typedef CvEM ExpectationMaximization;

/*!
 The Patch Generator class
 */
class CV_EXPORTS PatchGenerator
{
public:
    PatchGenerator();
    PatchGenerator(double _backgroundMin, double _backgroundMax,
                   double _noiseRange, bool _randomBlur=true,
                   double _lambdaMin=0.6, double _lambdaMax=1.5,
                   double _thetaMin=-CV_PI, double _thetaMax=CV_PI,
                   double _phiMin=-CV_PI, double _phiMax=CV_PI );
    void operator()(const Mat& image, Point2f pt, Mat& patch, Size patchSize, RNG& rng) const;
    void operator()(const Mat& image, const Mat& transform, Mat& patch,
                    Size patchSize, RNG& rng) const;
    void warpWholeImage(const Mat& image, Mat& matT, Mat& buf,
                        CV_OUT Mat& warped, int border, RNG& rng) const;
    void generateRandomTransform(Point2f srcCenter, Point2f dstCenter,
                                 CV_OUT Mat& transform, RNG& rng,
                                 bool inverse=false) const;
    void setAffineParam(double lambda, double theta, double phi);

    double backgroundMin, backgroundMax;
    double noiseRange;
    bool randomBlur;
    double lambdaMin, lambdaMax;
    double thetaMin, thetaMax;
    double phiMin, phiMax;
};


class CV_EXPORTS LDetector
{
public:
    LDetector();
    LDetector(int _radius, int _threshold, int _nOctaves,
              int _nViews, double _baseFeatureSize, double _clusteringDistance);
    void operator()(const Mat& image,
                    CV_OUT vector<KeyPoint>& keypoints,
                    int maxCount=0, bool scaleCoords=true) const;
    void operator()(const vector<Mat>& pyr,
                    CV_OUT vector<KeyPoint>& keypoints,
                    int maxCount=0, bool scaleCoords=true) const;
    void getMostStable2D(const Mat& image, CV_OUT vector<KeyPoint>& keypoints,
                         int maxCount, const PatchGenerator& patchGenerator) const;
    void setVerbose(bool verbose);

    void read(const FileNode& node);
    void write(FileStorage& fs, const String& name=String()) const;

    int radius;
    int threshold;
    int nOctaves;
    int nViews;
    bool verbose;

    double baseFeatureSize;
    double clusteringDistance;
};

typedef LDetector YAPE;

class CV_EXPORTS FernClassifier
{
public:
    FernClassifier();
    FernClassifier(const FileNode& node);
    FernClassifier(const vector<vector<Point2f> >& points,
                   const vector<Mat>& refimgs,
                   const vector<vector<int> >& labels=vector<vector<int> >(),
                   int _nclasses=0, int _patchSize=PATCH_SIZE,
                   int _signatureSize=DEFAULT_SIGNATURE_SIZE,
                   int _nstructs=DEFAULT_STRUCTS,
                   int _structSize=DEFAULT_STRUCT_SIZE,
                   int _nviews=DEFAULT_VIEWS,
                   int _compressionMethod=COMPRESSION_NONE,
                   const PatchGenerator& patchGenerator=PatchGenerator());
    virtual ~FernClassifier();
    virtual void read(const FileNode& n);
    virtual void write(FileStorage& fs, const String& name=String()) const;
    virtual void trainFromSingleView(const Mat& image,
                                     const vector<KeyPoint>& keypoints,
                                     int _patchSize=PATCH_SIZE,
                                     int _signatureSize=DEFAULT_SIGNATURE_SIZE,
                                     int _nstructs=DEFAULT_STRUCTS,
                                     int _structSize=DEFAULT_STRUCT_SIZE,
                                     int _nviews=DEFAULT_VIEWS,
                                     int _compressionMethod=COMPRESSION_NONE,
                                     const PatchGenerator& patchGenerator=PatchGenerator());
    virtual void train(const vector<vector<Point2f> >& points,
                       const vector<Mat>& refimgs,
                       const vector<vector<int> >& labels=vector<vector<int> >(),
                       int _nclasses=0, int _patchSize=PATCH_SIZE,
                       int _signatureSize=DEFAULT_SIGNATURE_SIZE,
                       int _nstructs=DEFAULT_STRUCTS,
                       int _structSize=DEFAULT_STRUCT_SIZE,
                       int _nviews=DEFAULT_VIEWS,
                       int _compressionMethod=COMPRESSION_NONE,
                       const PatchGenerator& patchGenerator=PatchGenerator());
    virtual int operator()(const Mat& img, Point2f kpt, vector<float>& signature) const;
    virtual int operator()(const Mat& patch, vector<float>& signature) const;
    virtual void clear();
    virtual bool empty() const;
    void setVerbose(bool verbose);

    int getClassCount() const;
    int getStructCount() const;
    int getStructSize() const;
    int getSignatureSize() const;
    int getCompressionMethod() const;
    Size getPatchSize() const;

    struct Feature
    {
        uchar x1, y1, x2, y2;
        Feature() : x1(0), y1(0), x2(0), y2(0) {}
        Feature(int _x1, int _y1, int _x2, int _y2)
        : x1((uchar)_x1), y1((uchar)_y1), x2((uchar)_x2), y2((uchar)_y2)
        {}
        template<typename _Tp> bool operator ()(const Mat_<_Tp>& patch) const
        { return patch(y1,x1) > patch(y2, x2); }
    };

    enum
    {
        PATCH_SIZE = 31,
        DEFAULT_STRUCTS = 50,
        DEFAULT_STRUCT_SIZE = 9,
        DEFAULT_VIEWS = 5000,
        DEFAULT_SIGNATURE_SIZE = 176,
        COMPRESSION_NONE = 0,
        COMPRESSION_RANDOM_PROJ = 1,
        COMPRESSION_PCA = 2,
        DEFAULT_COMPRESSION_METHOD = COMPRESSION_NONE
    };

protected:
    virtual void prepare(int _nclasses, int _patchSize, int _signatureSize,
                         int _nstructs, int _structSize,
                         int _nviews, int _compressionMethod);
    virtual void finalize(RNG& rng);
    virtual int getLeaf(int fidx, const Mat& patch) const;

    bool verbose;
    int nstructs;
    int structSize;
    int nclasses;
    int signatureSize;
    int compressionMethod;
    int leavesPerStruct;
    Size patchSize;
    vector<Feature> features;
    vector<int> classCounters;
    vector<float> posteriors;
};


/****************************************************************************************\
 *                                 Calonder Classifier                                    *
 \****************************************************************************************/

struct RTreeNode;

struct CV_EXPORTS BaseKeypoint
{
    int x;
    int y;
    IplImage* image;

    BaseKeypoint()
    : x(0), y(0), image(NULL)
    {}

    BaseKeypoint(int _x, int _y, IplImage* _image)
    : x(_x), y(_y), image(_image)
    {}
};

class CV_EXPORTS RandomizedTree
{
public:
    friend class RTreeClassifier;

    static const uchar PATCH_SIZE = 32;
    static const int DEFAULT_DEPTH = 9;
    static const int DEFAULT_VIEWS = 5000;
    static const size_t DEFAULT_REDUCED_NUM_DIM = 176;
    static float GET_LOWER_QUANT_PERC() { return .03f; }
    static float GET_UPPER_QUANT_PERC() { return .92f; }

    RandomizedTree();
    ~RandomizedTree();

    void train(vector<BaseKeypoint> const& base_set, RNG &rng,
               int depth, int views, size_t reduced_num_dim, int num_quant_bits);
    void train(vector<BaseKeypoint> const& base_set, RNG &rng,
               PatchGenerator &make_patch, int depth, int views, size_t reduced_num_dim,
               int num_quant_bits);

    // following two funcs are EXPERIMENTAL (do not use unless you know exactly what you do)
    static void quantizeVector(float *vec, int dim, int N, float bnds[2], int clamp_mode=0);
    static void quantizeVector(float *src, int dim, int N, float bnds[2], uchar *dst);

    // patch_data must be a 32x32 array (no row padding)
    float* getPosterior(uchar* patch_data);
    const float* getPosterior(uchar* patch_data) const;
    uchar* getPosterior2(uchar* patch_data);
    const uchar* getPosterior2(uchar* patch_data) const;

    void read(const char* file_name, int num_quant_bits);
    void read(std::istream &is, int num_quant_bits);
    void write(const char* file_name) const;
    void write(std::ostream &os) const;

    int classes() { return classes_; }
    int depth() { return depth_; }

    //void setKeepFloatPosteriors(bool b) { keep_float_posteriors_ = b; }
    void discardFloatPosteriors() { freePosteriors(1); }

    inline void applyQuantization(int num_quant_bits) { makePosteriors2(num_quant_bits); }

    // debug
    void savePosteriors(std::string url, bool append=false);
    void savePosteriors2(std::string url, bool append=false);

private:
    int classes_;
    int depth_;
    int num_leaves_;
    vector<RTreeNode> nodes_;
    float **posteriors_;        // 16-bytes aligned posteriors
    uchar **posteriors2_;     // 16-bytes aligned posteriors
    vector<int> leaf_counts_;

    void createNodes(int num_nodes, RNG &rng);
    void allocPosteriorsAligned(int num_leaves, int num_classes);
    void freePosteriors(int which);    // which: 1=posteriors_, 2=posteriors2_, 3=both
    void init(int classes, int depth, RNG &rng);
    void addExample(int class_id, uchar* patch_data);
    void finalize(size_t reduced_num_dim, int num_quant_bits);
    int getIndex(uchar* patch_data) const;
    inline float* getPosteriorByIndex(int index);
    inline const float* getPosteriorByIndex(int index) const;
    inline uchar* getPosteriorByIndex2(int index);
    inline const uchar* getPosteriorByIndex2(int index) const;
    //void makeRandomMeasMatrix(float *cs_phi, PHI_DISTR_TYPE dt, size_t reduced_num_dim);
    void convertPosteriorsToChar();
    void makePosteriors2(int num_quant_bits);
    void compressLeaves(size_t reduced_num_dim);
    void estimateQuantPercForPosteriors(float perc[2]);
};


inline uchar* getData(IplImage* image)
{
    return reinterpret_cast<uchar*>(image->imageData);
}

inline float* RandomizedTree::getPosteriorByIndex(int index)
{
    return const_cast<float*>(const_cast<const RandomizedTree*>(this)->getPosteriorByIndex(index));
}

inline const float* RandomizedTree::getPosteriorByIndex(int index) const
{
    return posteriors_[index];
}

inline uchar* RandomizedTree::getPosteriorByIndex2(int index)
{
    return const_cast<uchar*>(const_cast<const RandomizedTree*>(this)->getPosteriorByIndex2(index));
}

inline const uchar* RandomizedTree::getPosteriorByIndex2(int index) const
{
    return posteriors2_[index];
}

struct CV_EXPORTS RTreeNode
{
    short offset1, offset2;

    RTreeNode() {}
    RTreeNode(uchar x1, uchar y1, uchar x2, uchar y2)
    : offset1(y1*RandomizedTree::PATCH_SIZE + x1),
    offset2(y2*RandomizedTree::PATCH_SIZE + x2)
    {}

    //! Left child on 0, right child on 1
    inline bool operator() (uchar* patch_data) const
    {
        return patch_data[offset1] > patch_data[offset2];
    }
};

class CV_EXPORTS RTreeClassifier
{
public:
    static const int DEFAULT_TREES = 48;
    static const size_t DEFAULT_NUM_QUANT_BITS = 4;

    RTreeClassifier();
    void train(vector<BaseKeypoint> const& base_set,
               RNG &rng,
               int num_trees = RTreeClassifier::DEFAULT_TREES,
               int depth = RandomizedTree::DEFAULT_DEPTH,
               int views = RandomizedTree::DEFAULT_VIEWS,
               size_t reduced_num_dim = RandomizedTree::DEFAULT_REDUCED_NUM_DIM,
               int num_quant_bits = DEFAULT_NUM_QUANT_BITS);
    void train(vector<BaseKeypoint> const& base_set,
               RNG &rng,
               PatchGenerator &make_patch,
               int num_trees = RTreeClassifier::DEFAULT_TREES,
               int depth = RandomizedTree::DEFAULT_DEPTH,
               int views = RandomizedTree::DEFAULT_VIEWS,
               size_t reduced_num_dim = RandomizedTree::DEFAULT_REDUCED_NUM_DIM,
               int num_quant_bits = DEFAULT_NUM_QUANT_BITS);

    // sig must point to a memory block of at least classes()*sizeof(float|uchar) bytes
    void getSignature(IplImage *patch, uchar *sig) const;
    void getSignature(IplImage *patch, float *sig) const;
    void getSparseSignature(IplImage *patch, float *sig, float thresh) const;
    // TODO: deprecated in favor of getSignature overload, remove
    void getFloatSignature(IplImage *patch, float *sig) const { getSignature(patch, sig); }

    static int countNonZeroElements(float *vec, int n, double tol=1e-10);
    static inline void safeSignatureAlloc(uchar **sig, int num_sig=1, int sig_len=176);
    static inline uchar* safeSignatureAlloc(int num_sig=1, int sig_len=176);

    inline int classes() const { return classes_; }
    inline int original_num_classes() const { return original_num_classes_; }

    void setQuantization(int num_quant_bits);
    void discardFloatPosteriors();

    void read(const char* file_name);
    void read(std::istream &is);
    void write(const char* file_name) const;
    void write(std::ostream &os) const;

    // experimental and debug
    void saveAllFloatPosteriors(std::string file_url);
    void saveAllBytePosteriors(std::string file_url);
    void setFloatPosteriorsFromTextfile_176(std::string url);
    float countZeroElements();

    vector<RandomizedTree> trees_;

private:
    int classes_;
    int num_quant_bits_;
    mutable uchar **posteriors_;
    mutable unsigned short *ptemp_;
    int original_num_classes_;
    bool keep_floats_;
};

/****************************************************************************************\
*                                     One-Way Descriptor                                 *
\****************************************************************************************/

// CvAffinePose: defines a parameterized affine transformation of an image patch.
// An image patch is rotated on angle phi (in degrees), then scaled lambda1 times
// along horizontal and lambda2 times along vertical direction, and then rotated again
// on angle (theta - phi).
class CV_EXPORTS CvAffinePose
{
public:
    float phi;
    float theta;
    float lambda1;
    float lambda2;
};

class CV_EXPORTS OneWayDescriptor
{
public:
    OneWayDescriptor();
    ~OneWayDescriptor();

    // allocates memory for given descriptor parameters
    void Allocate(int pose_count, CvSize size, int nChannels);

    // GenerateSamples: generates affine transformed patches with averaging them over small transformation variations.
    // If external poses and transforms were specified, uses them instead of generating random ones
    // - pose_count: the number of poses to be generated
    // - frontal: the input patch (can be a roi in a larger image)
    // - norm: if nonzero, normalizes the output patch so that the sum of pixel intensities is 1
    void GenerateSamples(int pose_count, IplImage* frontal, int norm = 0);

    // GenerateSamplesFast: generates affine transformed patches with averaging them over small transformation variations.
    // Uses precalculated transformed pca components.
    // - frontal: the input patch (can be a roi in a larger image)
    // - pca_hr_avg: pca average vector
    // - pca_hr_eigenvectors: pca eigenvectors
    // - pca_descriptors: an array of precomputed descriptors of pca components containing their affine transformations
    //   pca_descriptors[0] corresponds to the average, pca_descriptors[1]-pca_descriptors[pca_dim] correspond to eigenvectors
    void GenerateSamplesFast(IplImage* frontal, CvMat* pca_hr_avg,
                             CvMat* pca_hr_eigenvectors, OneWayDescriptor* pca_descriptors);

    // sets the poses and corresponding transforms
    void SetTransforms(CvAffinePose* poses, CvMat** transforms);

    // Initialize: builds a descriptor.
    // - pose_count: the number of poses to build. If poses were set externally, uses them rather than generating random ones
    // - frontal: input patch. Can be a roi in a larger image
    // - feature_name: the feature name to be associated with the descriptor
    // - norm: if 1, the affine transformed patches are normalized so that their sum is 1
    void Initialize(int pose_count, IplImage* frontal, const char* feature_name = 0, int norm = 0);

    // InitializeFast: builds a descriptor using precomputed descriptors of pca components
    // - pose_count: the number of poses to build
    // - frontal: input patch. Can be a roi in a larger image
    // - feature_name: the feature name to be associated with the descriptor
    // - pca_hr_avg: average vector for PCA
    // - pca_hr_eigenvectors: PCA eigenvectors (one vector per row)
    // - pca_descriptors: precomputed descriptors of PCA components, the first descriptor for the average vector
    // followed by the descriptors for eigenvectors
    void InitializeFast(int pose_count, IplImage* frontal, const char* feature_name,
                        CvMat* pca_hr_avg, CvMat* pca_hr_eigenvectors, OneWayDescriptor* pca_descriptors);

    // ProjectPCASample: unwarps an image patch into a vector and projects it into PCA space
    // - patch: input image patch
    // - avg: PCA average vector
    // - eigenvectors: PCA eigenvectors, one per row
    // - pca_coeffs: output PCA coefficients
    void ProjectPCASample(IplImage* patch, CvMat* avg, CvMat* eigenvectors, CvMat* pca_coeffs) const;

    // InitializePCACoeffs: projects all warped patches into PCA space
    // - avg: PCA average vector
    // - eigenvectors: PCA eigenvectors, one per row
    void InitializePCACoeffs(CvMat* avg, CvMat* eigenvectors);

    // EstimatePose: finds the closest match between an input patch and a set of patches with different poses
    // - patch: input image patch
    // - pose_idx: the output index of the closest pose
    // - distance: the distance to the closest pose (L2 distance)
    void EstimatePose(IplImage* patch, int& pose_idx, float& distance) const;

    // EstimatePosePCA: finds the closest match between an input patch and a set of patches with different poses.
    // The distance between patches is computed in PCA space
    // - patch: input image patch
    // - pose_idx: the output index of the closest pose
    // - distance: distance to the closest pose (L2 distance in PCA space)
    // - avg: PCA average vector. If 0, matching without PCA is used
    // - eigenvectors: PCA eigenvectors, one per row
    void EstimatePosePCA(CvArr* patch, int& pose_idx, float& distance, CvMat* avg, CvMat* eigenvalues) const;

    // GetPatchSize: returns the size of each image patch after warping (2 times smaller than the input patch)
    CvSize GetPatchSize() const
    {
        return m_patch_size;
    }

    // GetInputPatchSize: returns the required size of the patch that the descriptor is built from
    // (2 time larger than the patch after warping)
    CvSize GetInputPatchSize() const
    {
        return cvSize(m_patch_size.width*2, m_patch_size.height*2);
    }

    // GetPatch: returns a patch corresponding to specified pose index
    // - index: pose index
    // - return value: the patch corresponding to specified pose index
    IplImage* GetPatch(int index);

    // GetPose: returns a pose corresponding to specified pose index
    // - index: pose index
    // - return value: the pose corresponding to specified pose index
    CvAffinePose GetPose(int index) const;

    // Save: saves all patches with different poses to a specified path
    void Save(const char* path);

    // ReadByName: reads a descriptor from a file storage
    // - fs: file storage
    // - parent: parent node
    // - name: node name
    // - return value: 1 if succeeded, 0 otherwise
    int ReadByName(CvFileStorage* fs, CvFileNode* parent, const char* name);

    // ReadByName: reads a descriptor from a file node
    // - parent: parent node
    // - name: node name
    // - return value: 1 if succeeded, 0 otherwise
    int ReadByName(const FileNode &parent, const char* name);

    // Write: writes a descriptor into a file storage
    // - fs: file storage
    // - name: node name
    void Write(CvFileStorage* fs, const char* name);

    // GetFeatureName: returns a name corresponding to a feature
    const char* GetFeatureName() const;

    // GetCenter: returns the center of the feature
    CvPoint GetCenter() const;

    void SetPCADimHigh(int pca_dim_high) {m_pca_dim_high = pca_dim_high;};
    void SetPCADimLow(int pca_dim_low) {m_pca_dim_low = pca_dim_low;};

    int GetPCADimLow() const;
    int GetPCADimHigh() const;

    CvMat** GetPCACoeffs() const {return m_pca_coeffs;}

protected:
    int m_pose_count; // the number of poses
    CvSize m_patch_size; // size of each image
    IplImage** m_samples; // an array of length m_pose_count containing the patch in different poses
    IplImage* m_input_patch;
    IplImage* m_train_patch;
    CvMat** m_pca_coeffs; // an array of length m_pose_count containing pca decomposition of the patch in different poses
    CvAffinePose* m_affine_poses; // an array of poses
    CvMat** m_transforms; // an array of affine transforms corresponding to poses

    string m_feature_name; // the name of the feature associated with the descriptor
    CvPoint m_center; // the coordinates of the feature (the center of the input image ROI)

    int m_pca_dim_high; // the number of descriptor pca components to use for generating affine poses
    int m_pca_dim_low; // the number of pca components to use for comparison
};


// OneWayDescriptorBase: encapsulates functionality for training/loading a set of one way descriptors
// and finding the nearest closest descriptor to an input feature
class CV_EXPORTS OneWayDescriptorBase
{
public:

    // creates an instance of OneWayDescriptor from a set of training files
    // - patch_size: size of the input (large) patch
    // - pose_count: the number of poses to generate for each descriptor
    // - train_path: path to training files
    // - pca_config: the name of the file that contains PCA for small patches (2 times smaller
    // than patch_size each dimension
    // - pca_hr_config: the name of the file that contains PCA for large patches (of patch_size size)
    // - pca_desc_config: the name of the file that contains descriptors of PCA components
    OneWayDescriptorBase(CvSize patch_size, int pose_count, const char* train_path = 0, const char* pca_config = 0,
                         const char* pca_hr_config = 0, const char* pca_desc_config = 0, int pyr_levels = 1,
                         int pca_dim_high = 100, int pca_dim_low = 100);

    OneWayDescriptorBase(CvSize patch_size, int pose_count, const string &pca_filename, const string &train_path = string(), const string &images_list = string(),
                         float _scale_min = 0.7f, float _scale_max=1.5f, float _scale_step=1.2f, int pyr_levels = 1,
                         int pca_dim_high = 100, int pca_dim_low = 100);


    virtual ~OneWayDescriptorBase();
    void clear ();


    // Allocate: allocates memory for a given number of descriptors
    void Allocate(int train_feature_count);

    // AllocatePCADescriptors: allocates memory for pca descriptors
    void AllocatePCADescriptors();

    // returns patch size
    CvSize GetPatchSize() const {return m_patch_size;};
    // returns the number of poses for each descriptor
    int GetPoseCount() const {return m_pose_count;};

    // returns the number of pyramid levels
    int GetPyrLevels() const {return m_pyr_levels;};

    // returns the number of descriptors
    int GetDescriptorCount() const {return m_train_feature_count;};

    // CreateDescriptorsFromImage: creates descriptors for each of the input features
    // - src: input image
    // - features: input features
    // - pyr_levels: the number of pyramid levels
    void CreateDescriptorsFromImage(IplImage* src, const vector<KeyPoint>& features);

    // CreatePCADescriptors: generates descriptors for PCA components, needed for fast generation of feature descriptors
    void CreatePCADescriptors();

    // returns a feature descriptor by feature index
    const OneWayDescriptor* GetDescriptor(int desc_idx) const {return &m_descriptors[desc_idx];};

    // FindDescriptor: finds the closest descriptor
    // - patch: input image patch
    // - desc_idx: output index of the closest descriptor to the input patch
    // - pose_idx: output index of the closest pose of the closest descriptor to the input patch
    // - distance: distance from the input patch to the closest feature pose
    // - _scales: scales of the input patch for each descriptor
    // - scale_ranges: input scales variation (float[2])
    void FindDescriptor(IplImage* patch, int& desc_idx, int& pose_idx, float& distance, float* _scale = 0, float* scale_ranges = 0) const;

    // - patch: input image patch
    // - n: number of the closest indexes
    // - desc_idxs: output indexes of the closest descriptor to the input patch (n)
    // - pose_idx: output indexes of the closest pose of the closest descriptor to the input patch (n)
    // - distances: distance from the input patch to the closest feature pose (n)
    // - _scales: scales of the input patch
    // - scale_ranges: input scales variation (float[2])
    void FindDescriptor(IplImage* patch, int n, vector<int>& desc_idxs, vector<int>& pose_idxs,
                        vector<float>& distances, vector<float>& _scales, float* scale_ranges = 0) const;

    // FindDescriptor: finds the closest descriptor
    // - src: input image
    // - pt: center of the feature
    // - desc_idx: output index of the closest descriptor to the input patch
    // - pose_idx: output index of the closest pose of the closest descriptor to the input patch
    // - distance: distance from the input patch to the closest feature pose
    void FindDescriptor(IplImage* src, cv::Point2f pt, int& desc_idx, int& pose_idx, float& distance) const;

    // InitializePoses: generates random poses
    void InitializePoses();

    // InitializeTransformsFromPoses: generates 2x3 affine matrices from poses (initializes m_transforms)
    void InitializeTransformsFromPoses();

    // InitializePoseTransforms: subsequently calls InitializePoses and InitializeTransformsFromPoses
    void InitializePoseTransforms();

    // InitializeDescriptor: initializes a descriptor
    // - desc_idx: descriptor index
    // - train_image: image patch (ROI is supported)
    // - feature_label: feature textual label
    void InitializeDescriptor(int desc_idx, IplImage* train_image, const char* feature_label);

    void InitializeDescriptor(int desc_idx, IplImage* train_image, const KeyPoint& keypoint, const char* feature_label);

    // InitializeDescriptors: load features from an image and create descriptors for each of them
    void InitializeDescriptors(IplImage* train_image, const vector<KeyPoint>& features,
                               const char* feature_label = "", int desc_start_idx = 0);

    // Write: writes this object to a file storage
    // - fs: output filestorage
    void Write (FileStorage &fs) const;

    // Read: reads OneWayDescriptorBase object from a file node
    // - fn: input file node
    void Read (const FileNode &fn);

    // LoadPCADescriptors: loads PCA descriptors from a file
    // - filename: input filename
    int LoadPCADescriptors(const char* filename);

    // LoadPCADescriptors: loads PCA descriptors from a file node
    // - fn: input file node
    int LoadPCADescriptors(const FileNode &fn);

    // SavePCADescriptors: saves PCA descriptors to a file
    // - filename: output filename
    void SavePCADescriptors(const char* filename);

    // SavePCADescriptors: saves PCA descriptors to a file storage
    // - fs: output file storage
    void SavePCADescriptors(CvFileStorage* fs) const;

    // GeneratePCA: calculate and save PCA components and descriptors
    // - img_path: path to training PCA images directory
    // - images_list: filename with filenames of training PCA images
    void GeneratePCA(const char* img_path, const char* images_list, int pose_count=500);

    // SetPCAHigh: sets the high resolution pca matrices (copied to internal structures)
    void SetPCAHigh(CvMat* avg, CvMat* eigenvectors);

    // SetPCALow: sets the low resolution pca matrices (copied to internal structures)
    void SetPCALow(CvMat* avg, CvMat* eigenvectors);

    int GetLowPCA(CvMat** avg, CvMat** eigenvectors)
    {
        *avg = m_pca_avg;
        *eigenvectors = m_pca_eigenvectors;
        return m_pca_dim_low;
    };

    int GetPCADimLow() const {return m_pca_dim_low;};
    int GetPCADimHigh() const {return m_pca_dim_high;};

    void ConvertDescriptorsArrayToTree(); // Converting pca_descriptors array to KD tree

    // GetPCAFilename: get default PCA filename
    static string GetPCAFilename () { return "pca.yml"; }

    virtual bool empty() const { return m_train_feature_count <= 0 ? true : false; }

protected:
    CvSize m_patch_size; // patch size
    int m_pose_count; // the number of poses for each descriptor
    int m_train_feature_count; // the number of the training features
    OneWayDescriptor* m_descriptors; // array of train feature descriptors
    CvMat* m_pca_avg; // PCA average Vector for small patches
    CvMat* m_pca_eigenvectors; // PCA eigenvectors for small patches
    CvMat* m_pca_hr_avg; // PCA average Vector for large patches
    CvMat* m_pca_hr_eigenvectors; // PCA eigenvectors for large patches
    OneWayDescriptor* m_pca_descriptors; // an array of PCA descriptors

    cv::flann::Index* m_pca_descriptors_tree;
    CvMat* m_pca_descriptors_matrix;

    CvAffinePose* m_poses; // array of poses
    CvMat** m_transforms; // array of affine transformations corresponding to poses

    int m_pca_dim_high;
    int m_pca_dim_low;

    int m_pyr_levels;
    float scale_min;
    float scale_max;
    float scale_step;

    // SavePCAall: saves PCA components and descriptors to a file storage
    // - fs: output file storage
    void SavePCAall (FileStorage &fs) const;

    // LoadPCAall: loads PCA components and descriptors from a file node
    // - fn: input file node
    void LoadPCAall (const FileNode &fn);
};

class CV_EXPORTS OneWayDescriptorObject : public OneWayDescriptorBase
{
public:
    // creates an instance of OneWayDescriptorObject from a set of training files
    // - patch_size: size of the input (large) patch
    // - pose_count: the number of poses to generate for each descriptor
    // - train_path: path to training files
    // - pca_config: the name of the file that contains PCA for small patches (2 times smaller
    // than patch_size each dimension
    // - pca_hr_config: the name of the file that contains PCA for large patches (of patch_size size)
    // - pca_desc_config: the name of the file that contains descriptors of PCA components
    OneWayDescriptorObject(CvSize patch_size, int pose_count, const char* train_path, const char* pca_config,
                           const char* pca_hr_config = 0, const char* pca_desc_config = 0, int pyr_levels = 1);

    OneWayDescriptorObject(CvSize patch_size, int pose_count, const string &pca_filename,
                           const string &train_path = string (), const string &images_list = string (),
                           float _scale_min = 0.7f, float _scale_max=1.5f, float _scale_step=1.2f, int pyr_levels = 1);


    virtual ~OneWayDescriptorObject();

    // Allocate: allocates memory for a given number of features
    // - train_feature_count: the total number of features
    // - object_feature_count: the number of features extracted from the object
    void Allocate(int train_feature_count, int object_feature_count);


    void SetLabeledFeatures(const vector<KeyPoint>& features) {m_train_features = features;};
    vector<KeyPoint>& GetLabeledFeatures() {return m_train_features;};
    const vector<KeyPoint>& GetLabeledFeatures() const {return m_train_features;};
    vector<KeyPoint> _GetLabeledFeatures() const;

    // IsDescriptorObject: returns 1 if descriptor with specified index is positive, otherwise 0
    int IsDescriptorObject(int desc_idx) const;

    // MatchPointToPart: returns the part number of a feature if it matches one of the object parts, otherwise -1
    int MatchPointToPart(CvPoint pt) const;

    // GetDescriptorPart: returns the part number of the feature corresponding to a specified descriptor
    // - desc_idx: descriptor index
    int GetDescriptorPart(int desc_idx) const;


    void InitializeObjectDescriptors(IplImage* train_image, const vector<KeyPoint>& features,
                                     const char* feature_label, int desc_start_idx = 0, float scale = 1.0f,
                                     int is_background = 0);

    // GetObjectFeatureCount: returns the number of object features
    int GetObjectFeatureCount() const {return m_object_feature_count;};

protected:
    int* m_part_id; // contains part id for each of object descriptors
    vector<KeyPoint> m_train_features; // train features
    int m_object_feature_count; // the number of the positive features

};


/*
 *  OneWayDescriptorMatcher
 */
class OneWayDescriptorMatcher;
typedef OneWayDescriptorMatcher OneWayDescriptorMatch;

class CV_EXPORTS OneWayDescriptorMatcher : public GenericDescriptorMatcher
{
public:
    class CV_EXPORTS Params
    {
    public:
        static const int POSE_COUNT = 500;
        static const int PATCH_WIDTH = 24;
        static const int PATCH_HEIGHT = 24;
        static float GET_MIN_SCALE() { return 0.7f; }
        static float GET_MAX_SCALE() { return 1.5f; }
        static float GET_STEP_SCALE() { return 1.2f; }

        Params( int poseCount = POSE_COUNT,
               Size patchSize = Size(PATCH_WIDTH, PATCH_HEIGHT),
               string pcaFilename = string(),
               string trainPath = string(), string trainImagesList = string(),
               float minScale = GET_MIN_SCALE(), float maxScale = GET_MAX_SCALE(),
               float stepScale = GET_STEP_SCALE() );

        int poseCount;
        Size patchSize;
        string pcaFilename;
        string trainPath;
        string trainImagesList;

        float minScale, maxScale, stepScale;
    };

    OneWayDescriptorMatcher( const Params& params=Params() );
    virtual ~OneWayDescriptorMatcher();

    void initialize( const Params& params, const Ptr<OneWayDescriptorBase>& base=Ptr<OneWayDescriptorBase>() );

    // Clears keypoints storing in collection and OneWayDescriptorBase
    virtual void clear();

    virtual void train();

    virtual bool isMaskSupported();

    virtual void read( const FileNode &fn );
    virtual void write( FileStorage& fs ) const;

    virtual bool empty() const;

    virtual Ptr<GenericDescriptorMatcher> clone( bool emptyTrainData=false ) const;

protected:
    // Matches a set of keypoints from a single image of the training set. A rectangle with a center in a keypoint
    // and size (patch_width/2*scale, patch_height/2*scale) is cropped from the source image for each
    // keypoint. scale is iterated from DescriptorOneWayParams::min_scale to DescriptorOneWayParams::max_scale.
    // The minimum distance to each training patch with all its affine poses is found over all scales.
    // The class ID of a match is returned for each keypoint. The distance is calculated over PCA components
    // loaded with DescriptorOneWay::Initialize, kd tree is used for finding minimum distances.
    virtual void knnMatchImpl( const Mat& queryImage, vector<KeyPoint>& queryKeypoints,
                              vector<vector<DMatch> >& matches, int k,
                              const vector<Mat>& masks, bool compactResult );
    virtual void radiusMatchImpl( const Mat& queryImage, vector<KeyPoint>& queryKeypoints,
                                 vector<vector<DMatch> >& matches, float maxDistance,
                                 const vector<Mat>& masks, bool compactResult );

    Ptr<OneWayDescriptorBase> base;
    Params params;
    int prevTrainCount;
};

/*
 *  FernDescriptorMatcher
 */
class FernDescriptorMatcher;
typedef FernDescriptorMatcher FernDescriptorMatch;

class CV_EXPORTS FernDescriptorMatcher : public GenericDescriptorMatcher
{
public:
    class CV_EXPORTS Params
    {
    public:
        Params( int nclasses=0,
               int patchSize=FernClassifier::PATCH_SIZE,
               int signatureSize=FernClassifier::DEFAULT_SIGNATURE_SIZE,
               int nstructs=FernClassifier::DEFAULT_STRUCTS,
               int structSize=FernClassifier::DEFAULT_STRUCT_SIZE,
               int nviews=FernClassifier::DEFAULT_VIEWS,
               int compressionMethod=FernClassifier::COMPRESSION_NONE,
               const PatchGenerator& patchGenerator=PatchGenerator() );

        Params( const string& filename );

        int nclasses;
        int patchSize;
        int signatureSize;
        int nstructs;
        int structSize;
        int nviews;
        int compressionMethod;
        PatchGenerator patchGenerator;

        string filename;
    };

    FernDescriptorMatcher( const Params& params=Params() );
    virtual ~FernDescriptorMatcher();

    virtual void clear();

    virtual void train();

    virtual bool isMaskSupported();

    virtual void read( const FileNode &fn );
    virtual void write( FileStorage& fs ) const;
    virtual bool empty() const;

    virtual Ptr<GenericDescriptorMatcher> clone( bool emptyTrainData=false ) const;

protected:
    virtual void knnMatchImpl( const Mat& queryImage, vector<KeyPoint>& queryKeypoints,
                              vector<vector<DMatch> >& matches, int k,
                              const vector<Mat>& masks, bool compactResult );
    virtual void radiusMatchImpl( const Mat& queryImage, vector<KeyPoint>& queryKeypoints,
                                 vector<vector<DMatch> >& matches, float maxDistance,
                                 const vector<Mat>& masks, bool compactResult );

    void trainFernClassifier();
    void calcBestProbAndMatchIdx( const Mat& image, const Point2f& pt,
                                 float& bestProb, int& bestMatchIdx, vector<float>& signature );
    Ptr<FernClassifier> classifier;
    Params params;
    int prevTrainCount;
};


/*
 * CalonderDescriptorExtractor
 */
template<typename T>
class CV_EXPORTS CalonderDescriptorExtractor : public DescriptorExtractor
{
public:
    CalonderDescriptorExtractor( const string& classifierFile );

    virtual void read( const FileNode &fn );
    virtual void write( FileStorage &fs ) const;

    virtual int descriptorSize() const { return classifier_.classes(); }
    virtual int descriptorType() const { return DataType<T>::type; }

    virtual bool empty() const;

protected:
    virtual void computeImpl( const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors ) const;

    RTreeClassifier classifier_;
    static const int BORDER_SIZE = 16;
};

template<typename T>
CalonderDescriptorExtractor<T>::CalonderDescriptorExtractor(const std::string& classifier_file)
{
    classifier_.read( classifier_file.c_str() );
}

template<typename T>
void CalonderDescriptorExtractor<T>::computeImpl( const Mat& image,
                                                 vector<KeyPoint>& keypoints,
                                                 Mat& descriptors) const
{
    // Cannot compute descriptors for keypoints on the image border.
    KeyPointsFilter::runByImageBorder(keypoints, image.size(), BORDER_SIZE);

    /// @todo Check 16-byte aligned
    descriptors.create((int)keypoints.size(), classifier_.classes(), cv::DataType<T>::type);

    int patchSize = RandomizedTree::PATCH_SIZE;
    int offset = patchSize / 2;
    for (size_t i = 0; i < keypoints.size(); ++i)
    {
        cv::Point2f pt = keypoints[i].pt;
        IplImage ipl = image( Rect((int)(pt.x - offset), (int)(pt.y - offset), patchSize, patchSize) );
        classifier_.getSignature( &ipl, descriptors.ptr<T>((int)i));
    }
}

template<typename T>
void CalonderDescriptorExtractor<T>::read( const FileNode& )
{}

template<typename T>
void CalonderDescriptorExtractor<T>::write( FileStorage& ) const
{}

template<typename T>
bool CalonderDescriptorExtractor<T>::empty() const
{
    return classifier_.trees_.empty();
}


////////////////////// Brute Force Matcher //////////////////////////

template<class Distance>
class CV_EXPORTS BruteForceMatcher : public BFMatcher
{
public:
    BruteForceMatcher( Distance d = Distance() ) : BFMatcher(Distance::normType, false) {(void)d;}
    virtual ~BruteForceMatcher() {}
};


/****************************************************************************************\
*                                Planar Object Detection                                 *
\****************************************************************************************/

class CV_EXPORTS PlanarObjectDetector
{
public:
    PlanarObjectDetector();
    PlanarObjectDetector(const FileNode& node);
    PlanarObjectDetector(const vector<Mat>& pyr, int _npoints=300,
                         int _patchSize=FernClassifier::PATCH_SIZE,
                         int _nstructs=FernClassifier::DEFAULT_STRUCTS,
                         int _structSize=FernClassifier::DEFAULT_STRUCT_SIZE,
                         int _nviews=FernClassifier::DEFAULT_VIEWS,
                         const LDetector& detector=LDetector(),
                         const PatchGenerator& patchGenerator=PatchGenerator());
    virtual ~PlanarObjectDetector();
    virtual void train(const vector<Mat>& pyr, int _npoints=300,
                       int _patchSize=FernClassifier::PATCH_SIZE,
                       int _nstructs=FernClassifier::DEFAULT_STRUCTS,
                       int _structSize=FernClassifier::DEFAULT_STRUCT_SIZE,
                       int _nviews=FernClassifier::DEFAULT_VIEWS,
                       const LDetector& detector=LDetector(),
                       const PatchGenerator& patchGenerator=PatchGenerator());
    virtual void train(const vector<Mat>& pyr, const vector<KeyPoint>& keypoints,
                       int _patchSize=FernClassifier::PATCH_SIZE,
                       int _nstructs=FernClassifier::DEFAULT_STRUCTS,
                       int _structSize=FernClassifier::DEFAULT_STRUCT_SIZE,
                       int _nviews=FernClassifier::DEFAULT_VIEWS,
                       const LDetector& detector=LDetector(),
                       const PatchGenerator& patchGenerator=PatchGenerator());
    Rect getModelROI() const;
    vector<KeyPoint> getModelPoints() const;
    const LDetector& getDetector() const;
    const FernClassifier& getClassifier() const;
    void setVerbose(bool verbose);

    void read(const FileNode& node);
    void write(FileStorage& fs, const String& name=String()) const;
    bool operator()(const Mat& image, CV_OUT Mat& H, CV_OUT vector<Point2f>& corners) const;
    bool operator()(const vector<Mat>& pyr, const vector<KeyPoint>& keypoints,
                    CV_OUT Mat& H, CV_OUT vector<Point2f>& corners,
                    CV_OUT vector<int>* pairs=0) const;

protected:
    bool verbose;
    Rect modelROI;
    vector<KeyPoint> modelPoints;
    LDetector ldetector;
    FernClassifier fernClassifier;
};

}

// 2009-01-12, Xavier Delacour <xavier.delacour@gmail.com>

struct lsh_hash {
    int h1, h2;
};

struct CvLSHOperations
{
    virtual ~CvLSHOperations() {}

    virtual int vector_add(const void* data) = 0;
    virtual void vector_remove(int i) = 0;
    virtual const void* vector_lookup(int i) = 0;
    virtual void vector_reserve(int n) = 0;
    virtual unsigned int vector_count() = 0;

    virtual void hash_insert(lsh_hash h, int l, int i) = 0;
    virtual void hash_remove(lsh_hash h, int l, int i) = 0;
    virtual int hash_lookup(lsh_hash h, int l, int* ret_i, int ret_i_max) = 0;
};

#endif

#ifdef __cplusplus
extern "C" {
#endif

/* Splits color or grayscale image into multiple connected components
 of nearly the same color/brightness using modification of Burt algorithm.
 comp with contain a pointer to sequence (CvSeq)
 of connected components (CvConnectedComp) */
CVAPI(void) cvPyrSegmentation( IplImage* src, IplImage* dst,
                              CvMemStorage* storage, CvSeq** comp,
                              int level, double threshold1,
                              double threshold2 );

/****************************************************************************************\
*                              Planar subdivisions                                       *
\****************************************************************************************/

/* Initializes Delaunay triangulation */
CVAPI(void)  cvInitSubdivDelaunay2D( CvSubdiv2D* subdiv, CvRect rect );

/* Creates new subdivision */
CVAPI(CvSubdiv2D*)  cvCreateSubdiv2D( int subdiv_type, int header_size,
                                     int vtx_size, int quadedge_size,
                                     CvMemStorage* storage );

/************************* high-level subdivision functions ***************************/

/* Simplified Delaunay diagram creation */
CV_INLINE  CvSubdiv2D* cvCreateSubdivDelaunay2D( CvRect rect, CvMemStorage* storage )
{
    CvSubdiv2D* subdiv = cvCreateSubdiv2D( CV_SEQ_KIND_SUBDIV2D, sizeof(*subdiv),
                                          sizeof(CvSubdiv2DPoint), sizeof(CvQuadEdge2D), storage );

    cvInitSubdivDelaunay2D( subdiv, rect );
    return subdiv;
}


/* Inserts new point to the Delaunay triangulation */
CVAPI(CvSubdiv2DPoint*)  cvSubdivDelaunay2DInsert( CvSubdiv2D* subdiv, CvPoint2D32f pt);

/* Locates a point within the Delaunay triangulation (finds the edge
 the point is left to or belongs to, or the triangulation point the given
 point coinsides with */
CVAPI(CvSubdiv2DPointLocation)  cvSubdiv2DLocate(
                                                 CvSubdiv2D* subdiv, CvPoint2D32f pt,
                                                 CvSubdiv2DEdge* edge,
                                                 CvSubdiv2DPoint** vertex CV_DEFAULT(NULL) );

/* Calculates Voronoi tesselation (i.e. coordinates of Voronoi points) */
CVAPI(void)  cvCalcSubdivVoronoi2D( CvSubdiv2D* subdiv );


/* Removes all Voronoi points from the tesselation */
CVAPI(void)  cvClearSubdivVoronoi2D( CvSubdiv2D* subdiv );


/* Finds the nearest to the given point vertex in subdivision. */
CVAPI(CvSubdiv2DPoint*) cvFindNearestPoint2D( CvSubdiv2D* subdiv, CvPoint2D32f pt );


/************ Basic quad-edge navigation and operations ************/

CV_INLINE  CvSubdiv2DEdge  cvSubdiv2DNextEdge( CvSubdiv2DEdge edge )
{
    return  CV_SUBDIV2D_NEXT_EDGE(edge);
}


CV_INLINE  CvSubdiv2DEdge  cvSubdiv2DRotateEdge( CvSubdiv2DEdge edge, int rotate )
{
    return  (edge & ~3) + ((edge + rotate) & 3);
}

CV_INLINE  CvSubdiv2DEdge  cvSubdiv2DSymEdge( CvSubdiv2DEdge edge )
{
    return edge ^ 2;
}

CV_INLINE  CvSubdiv2DEdge  cvSubdiv2DGetEdge( CvSubdiv2DEdge edge, CvNextEdgeType type )
{
    CvQuadEdge2D* e = (CvQuadEdge2D*)(edge & ~3);
    edge = e->next[(edge + (int)type) & 3];
    return  (edge & ~3) + ((edge + ((int)type >> 4)) & 3);
}


CV_INLINE  CvSubdiv2DPoint*  cvSubdiv2DEdgeOrg( CvSubdiv2DEdge edge )
{
    CvQuadEdge2D* e = (CvQuadEdge2D*)(edge & ~3);
    return (CvSubdiv2DPoint*)e->pt[edge & 3];
}


CV_INLINE  CvSubdiv2DPoint*  cvSubdiv2DEdgeDst( CvSubdiv2DEdge edge )
{
    CvQuadEdge2D* e = (CvQuadEdge2D*)(edge & ~3);
    return (CvSubdiv2DPoint*)e->pt[(edge + 2) & 3];
}

/****************************************************************************************\
*                           Additional operations on Subdivisions                        *
\****************************************************************************************/

// paints voronoi diagram: just demo function
CVAPI(void)  icvDrawMosaic( CvSubdiv2D* subdiv, IplImage* src, IplImage* dst );

// checks planar subdivision for correctness. It is not an absolute check,
// but it verifies some relations between quad-edges
CVAPI(int)   icvSubdiv2DCheck( CvSubdiv2D* subdiv );

// returns squared distance between two 2D points with floating-point coordinates.
CV_INLINE double icvSqDist2D32f( CvPoint2D32f pt1, CvPoint2D32f pt2 )
{
    double dx = pt1.x - pt2.x;
    double dy = pt1.y - pt2.y;

    return dx*dx + dy*dy;
}




CV_INLINE  double  cvTriangleArea( CvPoint2D32f a, CvPoint2D32f b, CvPoint2D32f c )
{
    return ((double)b.x - a.x) * ((double)c.y - a.y) - ((double)b.y - a.y) * ((double)c.x - a.x);
}


/* Constructs kd-tree from set of feature descriptors */
CVAPI(struct CvFeatureTree*) cvCreateKDTree(CvMat* desc);

/* Constructs spill-tree from set of feature descriptors */
CVAPI(struct CvFeatureTree*) cvCreateSpillTree( const CvMat* raw_data,
                                               const int naive CV_DEFAULT(50),
                                               const double rho CV_DEFAULT(.7),
                                               const double tau CV_DEFAULT(.1) );

/* Release feature tree */
CVAPI(void) cvReleaseFeatureTree(struct CvFeatureTree* tr);

/* Searches feature tree for k nearest neighbors of given reference points,
 searching (in case of kd-tree/bbf) at most emax leaves. */
CVAPI(void) cvFindFeatures(struct CvFeatureTree* tr, const CvMat* query_points,
                           CvMat* indices, CvMat* dist, int k, int emax CV_DEFAULT(20));

/* Search feature tree for all points that are inlier to given rect region.
 Only implemented for kd trees */
CVAPI(int) cvFindFeaturesBoxed(struct CvFeatureTree* tr,
                               CvMat* bounds_min, CvMat* bounds_max,
                               CvMat* out_indices);


/* Construct a Locality Sensitive Hash (LSH) table, for indexing d-dimensional vectors of
 given type. Vectors will be hashed L times with k-dimensional p-stable (p=2) functions. */
CVAPI(struct CvLSH*) cvCreateLSH(struct CvLSHOperations* ops, int d,
                                 int L CV_DEFAULT(10), int k CV_DEFAULT(10),
                                 int type CV_DEFAULT(CV_64FC1), double r CV_DEFAULT(4),
                                 int64 seed CV_DEFAULT(-1));

/* Construct in-memory LSH table, with n bins. */
CVAPI(struct CvLSH*) cvCreateMemoryLSH(int d, int n, int L CV_DEFAULT(10), int k CV_DEFAULT(10),
                                       int type CV_DEFAULT(CV_64FC1), double r CV_DEFAULT(4),
                                       int64 seed CV_DEFAULT(-1));

/* Free the given LSH structure. */
CVAPI(void) cvReleaseLSH(struct CvLSH** lsh);

/* Return the number of vectors in the LSH. */
CVAPI(unsigned int) LSHSize(struct CvLSH* lsh);

/* Add vectors to the LSH structure, optionally returning indices. */
CVAPI(void) cvLSHAdd(struct CvLSH* lsh, const CvMat* data, CvMat* indices CV_DEFAULT(0));

/* Remove vectors from LSH, as addressed by given indices. */
CVAPI(void) cvLSHRemove(struct CvLSH* lsh, const CvMat* indices);

/* Query the LSH n times for at most k nearest points; data is n x d,
 indices and dist are n x k. At most emax stored points will be accessed. */
CVAPI(void) cvLSHQuery(struct CvLSH* lsh, const CvMat* query_points,
                       CvMat* indices, CvMat* dist, int k, int emax);

/* Kolmogorov-Zabin stereo-correspondence algorithm (a.k.a. KZ1) */
#define CV_STEREO_GC_OCCLUDED  SHRT_MAX

typedef struct CvStereoGCState
{
    int Ithreshold;
    int interactionRadius;
    float K, lambda, lambda1, lambda2;
    int occlusionCost;
    int minDisparity;
    int numberOfDisparities;
    int maxIters;

    CvMat* left;
    CvMat* right;
    CvMat* dispLeft;
    CvMat* dispRight;
    CvMat* ptrLeft;
    CvMat* ptrRight;
    CvMat* vtxBuf;
    CvMat* edgeBuf;
} CvStereoGCState;

CVAPI(CvStereoGCState*) cvCreateStereoGCState( int numberOfDisparities, int maxIters );
CVAPI(void) cvReleaseStereoGCState( CvStereoGCState** state );

CVAPI(void) cvFindStereoCorrespondenceGC( const CvArr* left, const CvArr* right,
                                         CvArr* disparityLeft, CvArr* disparityRight,
                                         CvStereoGCState* state,
                                         int useDisparityGuess CV_DEFAULT(0) );

/* Calculates optical flow for 2 images using classical Lucas & Kanade algorithm */
CVAPI(void)  cvCalcOpticalFlowLK( const CvArr* prev, const CvArr* curr,
                                 CvSize win_size, CvArr* velx, CvArr* vely );

/* Calculates optical flow for 2 images using block matching algorithm */
CVAPI(void)  cvCalcOpticalFlowBM( const CvArr* prev, const CvArr* curr,
                                 CvSize block_size, CvSize shift_size,
                                 CvSize max_range, int use_previous,
                                 CvArr* velx, CvArr* vely );

/* Calculates Optical flow for 2 images using Horn & Schunck algorithm */
CVAPI(void)  cvCalcOpticalFlowHS( const CvArr* prev, const CvArr* curr,
                                 int use_previous, CvArr* velx, CvArr* vely,
                                 double lambda, CvTermCriteria criteria );


/****************************************************************************************\
*                           Background/foreground segmentation                           *
\****************************************************************************************/

/* We discriminate between foreground and background pixels
 * by building and maintaining a model of the background.
 * Any pixel which does not fit this model is then deemed
 * to be foreground.
 *
 * At present we support two core background models,
 * one of which has two variations:
 *
 *  o CV_BG_MODEL_FGD: latest and greatest algorithm, described in
 *
 *	 Foreground Object Detection from Videos Containing Complex Background.
 *	 Liyuan Li, Weimin Huang, Irene Y.H. Gu, and Qi Tian.
 *	 ACM MM2003 9p
 *
 *  o CV_BG_MODEL_FGD_SIMPLE:
 *       A code comment describes this as a simplified version of the above,
 *       but the code is in fact currently identical
 *
 *  o CV_BG_MODEL_MOG: "Mixture of Gaussians", older algorithm, described in
 *
 *       Moving target classification and tracking from real-time video.
 *       A Lipton, H Fujijoshi, R Patil
 *       Proceedings IEEE Workshop on Application of Computer Vision pp 8-14 1998
 *
 *       Learning patterns of activity using real-time tracking
 *       C Stauffer and W Grimson  August 2000
 *       IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8):747-757
 */


#define CV_BG_MODEL_FGD		0
#define CV_BG_MODEL_MOG		1			/* "Mixture of Gaussians".	*/
#define CV_BG_MODEL_FGD_SIMPLE	2

struct CvBGStatModel;

typedef void (CV_CDECL * CvReleaseBGStatModel)( struct CvBGStatModel** bg_model );
typedef int (CV_CDECL * CvUpdateBGStatModel)( IplImage* curr_frame, struct CvBGStatModel* bg_model,
                                             double learningRate );

#define CV_BG_STAT_MODEL_FIELDS()                                               \
int             type; /*type of BG model*/                                      \
CvReleaseBGStatModel release;                                                   \
CvUpdateBGStatModel update;                                                     \
IplImage*       background;   /*8UC3 reference background image*/               \
IplImage*       foreground;   /*8UC1 foreground image*/                         \
IplImage**      layers;       /*8UC3 reference background image, can be null */ \
int             layer_count;  /* can be zero */                                 \
CvMemStorage*   storage;      /*storage for foreground_regions*/                \
CvSeq*          foreground_regions /*foreground object contours*/

typedef struct CvBGStatModel
{
    CV_BG_STAT_MODEL_FIELDS();
} CvBGStatModel;

//

// Releases memory used by BGStatModel
CVAPI(void) cvReleaseBGStatModel( CvBGStatModel** bg_model );

// Updates statistical model and returns number of found foreground regions
CVAPI(int) cvUpdateBGStatModel( IplImage* current_frame, CvBGStatModel*  bg_model,
                               double learningRate CV_DEFAULT(-1));

// Performs FG post-processing using segmentation
// (all pixels of a region will be classified as foreground if majority of pixels of the region are FG).
// parameters:
//      segments - pointer to result of segmentation (for example MeanShiftSegmentation)
//      bg_model - pointer to CvBGStatModel structure
CVAPI(void) cvRefineForegroundMaskBySegm( CvSeq* segments, CvBGStatModel*  bg_model );

/* Common use change detection function */
CVAPI(int)  cvChangeDetection( IplImage*  prev_frame,
                              IplImage*  curr_frame,
                              IplImage*  change_mask );

/*
 Interface of ACM MM2003 algorithm
 */

/* Default parameters of foreground detection algorithm: */
#define  CV_BGFG_FGD_LC              128
#define  CV_BGFG_FGD_N1C             15
#define  CV_BGFG_FGD_N2C             25

#define  CV_BGFG_FGD_LCC             64
#define  CV_BGFG_FGD_N1CC            25
#define  CV_BGFG_FGD_N2CC            40

/* Background reference image update parameter: */
#define  CV_BGFG_FGD_ALPHA_1         0.1f

/* stat model update parameter
 * 0.002f ~ 1K frame(~45sec), 0.005 ~ 18sec (if 25fps and absolutely static BG)
 */
#define  CV_BGFG_FGD_ALPHA_2         0.005f

/* start value for alpha parameter (to fast initiate statistic model) */
#define  CV_BGFG_FGD_ALPHA_3         0.1f

#define  CV_BGFG_FGD_DELTA           2

#define  CV_BGFG_FGD_T               0.9f

#define  CV_BGFG_FGD_MINAREA         15.f

#define  CV_BGFG_FGD_BG_UPDATE_TRESH 0.5f

/* See the above-referenced Li/Huang/Gu/Tian paper
 * for a full description of these background-model
 * tuning parameters.
 *
 * Nomenclature:  'c'  == "color", a three-component red/green/blue vector.
 *                         We use histograms of these to model the range of
 *                         colors we've seen at a given background pixel.
 *
 *                'cc' == "color co-occurrence", a six-component vector giving
 *                         RGB color for both this frame and preceding frame.
 *                             We use histograms of these to model the range of
 *                         color CHANGES we've seen at a given background pixel.
 */
typedef struct CvFGDStatModelParams
{
    int    Lc;			/* Quantized levels per 'color' component. Power of two, typically 32, 64 or 128.				*/
    int    N1c;			/* Number of color vectors used to model normal background color variation at a given pixel.			*/
    int    N2c;			/* Number of color vectors retained at given pixel.  Must be > N1c, typically ~ 5/3 of N1c.			*/
    /* Used to allow the first N1c vectors to adapt over time to changing background.				*/

    int    Lcc;			/* Quantized levels per 'color co-occurrence' component.  Power of two, typically 16, 32 or 64.			*/
    int    N1cc;		/* Number of color co-occurrence vectors used to model normal background color variation at a given pixel.	*/
    int    N2cc;		/* Number of color co-occurrence vectors retained at given pixel.  Must be > N1cc, typically ~ 5/3 of N1cc.	*/
    /* Used to allow the first N1cc vectors to adapt over time to changing background.				*/

    int    is_obj_without_holes;/* If TRUE we ignore holes within foreground blobs. Defaults to TRUE.						*/
    int    perform_morphing;	/* Number of erode-dilate-erode foreground-blob cleanup iterations.						*/
    /* These erase one-pixel junk blobs and merge almost-touching blobs. Default value is 1.			*/

    float  alpha1;		/* How quickly we forget old background pixel values seen.  Typically set to 0.1  				*/
    float  alpha2;		/* "Controls speed of feature learning". Depends on T. Typical value circa 0.005. 				*/
    float  alpha3;		/* Alternate to alpha2, used (e.g.) for quicker initial convergence. Typical value 0.1.				*/

    float  delta;		/* Affects color and color co-occurrence quantization, typically set to 2.					*/
    float  T;			/* "A percentage value which determines when new features can be recognized as new background." (Typically 0.9).*/
    float  minArea;		/* Discard foreground blobs whose bounding box is smaller than this threshold.					*/
} CvFGDStatModelParams;

typedef struct CvBGPixelCStatTable
{
    float          Pv, Pvb;
    uchar          v[3];
} CvBGPixelCStatTable;

typedef struct CvBGPixelCCStatTable
{
    float          Pv, Pvb;
    uchar          v[6];
} CvBGPixelCCStatTable;

typedef struct CvBGPixelStat
{
    float                 Pbc;
    float                 Pbcc;
    CvBGPixelCStatTable*  ctable;
    CvBGPixelCCStatTable* cctable;
    uchar                 is_trained_st_model;
    uchar                 is_trained_dyn_model;
} CvBGPixelStat;


typedef struct CvFGDStatModel
{
    CV_BG_STAT_MODEL_FIELDS();
    CvBGPixelStat*         pixel_stat;
    IplImage*              Ftd;
    IplImage*              Fbd;
    IplImage*              prev_frame;
    CvFGDStatModelParams   params;
} CvFGDStatModel;

/* Creates FGD model */
CVAPI(CvBGStatModel*) cvCreateFGDStatModel( IplImage* first_frame,
                                           CvFGDStatModelParams* parameters CV_DEFAULT(NULL));

/*
 Interface of Gaussian mixture algorithm

 "An improved adaptive background mixture model for real-time tracking with shadow detection"
 P. KadewTraKuPong and R. Bowden,
 Proc. 2nd European Workshp on Advanced Video-Based Surveillance Systems, 2001."
 http://personal.ee.surrey.ac.uk/Personal/R.Bowden/publications/avbs01/avbs01.pdf
 */

/* Note:  "MOG" == "Mixture Of Gaussians": */

#define CV_BGFG_MOG_MAX_NGAUSSIANS 500

/* default parameters of gaussian background detection algorithm */
#define CV_BGFG_MOG_BACKGROUND_THRESHOLD     0.7     /* threshold sum of weights for background test */
#define CV_BGFG_MOG_STD_THRESHOLD            2.5     /* lambda=2.5 is 99% */
#define CV_BGFG_MOG_WINDOW_SIZE              200     /* Learning rate; alpha = 1/CV_GBG_WINDOW_SIZE */
#define CV_BGFG_MOG_NGAUSSIANS               5       /* = K = number of Gaussians in mixture */
#define CV_BGFG_MOG_WEIGHT_INIT              0.05
#define CV_BGFG_MOG_SIGMA_INIT               30
#define CV_BGFG_MOG_MINAREA                  15.f


#define CV_BGFG_MOG_NCOLORS                  3

typedef struct CvGaussBGStatModelParams
{
    int     win_size;               /* = 1/alpha */
    int     n_gauss;
    double  bg_threshold, std_threshold, minArea;
    double  weight_init, variance_init;
}CvGaussBGStatModelParams;

typedef struct CvGaussBGValues
{
    int         match_sum;
    double      weight;
    double      variance[CV_BGFG_MOG_NCOLORS];
    double      mean[CV_BGFG_MOG_NCOLORS];
} CvGaussBGValues;

typedef struct CvGaussBGPoint
{
    CvGaussBGValues* g_values;
} CvGaussBGPoint;


typedef struct CvGaussBGModel
{
    CV_BG_STAT_MODEL_FIELDS();
    CvGaussBGStatModelParams   params;
    CvGaussBGPoint*            g_point;
    int                        countFrames;
    void*                      mog;
} CvGaussBGModel;


/* Creates Gaussian mixture background model */
CVAPI(CvBGStatModel*) cvCreateGaussianBGModel( IplImage* first_frame,
                                              CvGaussBGStatModelParams* parameters CV_DEFAULT(NULL));


typedef struct CvBGCodeBookElem
{
    struct CvBGCodeBookElem* next;
    int tLastUpdate;
    int stale;
    uchar boxMin[3];
    uchar boxMax[3];
    uchar learnMin[3];
    uchar learnMax[3];
} CvBGCodeBookElem;

typedef struct CvBGCodeBookModel
{
    CvSize size;
    int t;
    uchar cbBounds[3];
    uchar modMin[3];
    uchar modMax[3];
    CvBGCodeBookElem** cbmap;
    CvMemStorage* storage;
    CvBGCodeBookElem* freeList;
} CvBGCodeBookModel;

CVAPI(CvBGCodeBookModel*) cvCreateBGCodeBookModel( void );
CVAPI(void) cvReleaseBGCodeBookModel( CvBGCodeBookModel** model );

CVAPI(void) cvBGCodeBookUpdate( CvBGCodeBookModel* model, const CvArr* image,
                               CvRect roi CV_DEFAULT(cvRect(0,0,0,0)),
                               const CvArr* mask CV_DEFAULT(0) );

CVAPI(int) cvBGCodeBookDiff( const CvBGCodeBookModel* model, const CvArr* image,
                            CvArr* fgmask, CvRect roi CV_DEFAULT(cvRect(0,0,0,0)) );

CVAPI(void) cvBGCodeBookClearStale( CvBGCodeBookModel* model, int staleThresh,
                                   CvRect roi CV_DEFAULT(cvRect(0,0,0,0)),
                                   const CvArr* mask CV_DEFAULT(0) );

CVAPI(CvSeq*) cvSegmentFGMask( CvArr *fgmask, int poly1Hull0 CV_DEFAULT(1),
                              float perimScale CV_DEFAULT(4.f),
                              CvMemStorage* storage CV_DEFAULT(0),
                              CvPoint offset CV_DEFAULT(cvPoint(0,0)));

#ifdef __cplusplus
}
#endif

#endif

/* End of file. */