📦 robzwolf / sm-image-processing

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10788# encoding: utf-8
# module cv2.cv2
# from D:\Robbie\Documents\dev\sm-image-processing\venv1\lib\site-packages\cv2\cv2.cp36-win32.pyd
# by generator 1.145
""" Python wrapper for OpenCV. """

# imports
import cv2.cv2 as  # D:\Robbie\Documents\dev\sm-image-processing\venv1\lib\site-packages\cv2\cv2.cp36-win32.pyd
import cv2.Error as Error # <module 'cv2.Error'>
import cv2.detail as detail # <module 'cv2.detail'>
import cv2.dnn as dnn # <module 'cv2.dnn'>
import cv2.fisheye as fisheye # <module 'cv2.fisheye'>
import cv2.flann as flann # <module 'cv2.flann'>
import cv2.instr as instr # <module 'cv2.instr'>
import cv2.ipp as ipp # <module 'cv2.ipp'>
import cv2.ml as ml # <module 'cv2.ml'>
import cv2.ocl as ocl # <module 'cv2.ocl'>
import cv2.ogl as ogl # <module 'cv2.ogl'>
import cv2.videostab as videostab # <module 'cv2.videostab'>

# Variables with simple values

ACCESS_FAST = 67108864
ACCESS_MASK = 50331648
ACCESS_READ = 16777216
ACCESS_RW = 50331648
ACCESS_WRITE = 33554432

ADAPTIVE_THRESH_GAUSSIAN_C = 1

ADAPTIVE_THRESH_MEAN_C = 0

AgastFeatureDetector_AGAST_5_8 = 0

AgastFeatureDetector_AGAST_7_12d = 1
AgastFeatureDetector_AGAST_7_12s = 2

AgastFeatureDetector_NONMAX_SUPPRESSION = 10001

AgastFeatureDetector_OAST_9_16 = 3

AgastFeatureDetector_THRESHOLD = 10000

AGAST_FEATURE_DETECTOR_AGAST_5_8 = 0

AGAST_FEATURE_DETECTOR_AGAST_7_12D = 1
AGAST_FEATURE_DETECTOR_AGAST_7_12S = 2

AGAST_FEATURE_DETECTOR_NONMAX_SUPPRESSION = 10001

AGAST_FEATURE_DETECTOR_OAST_9_16 = 3

AGAST_FEATURE_DETECTOR_THRESHOLD = 10000

AKAZE_DESCRIPTOR_KAZE = 3

AKAZE_DESCRIPTOR_KAZE_UPRIGHT = 2

AKAZE_DESCRIPTOR_MLDB = 5

AKAZE_DESCRIPTOR_MLDB_UPRIGHT = 4

BORDER_CONSTANT = 0
BORDER_DEFAULT = 4
BORDER_ISOLATED = 16
BORDER_REFLECT = 2
BORDER_REFLECT101 = 4

BORDER_REFLECT_101 = 4

BORDER_REPLICATE = 1
BORDER_TRANSPARENT = 5
BORDER_WRAP = 3

CALIB_CB_ADAPTIVE_THRESH = 1

CALIB_CB_ASYMMETRIC_GRID = 2

CALIB_CB_CLUSTERING = 4

CALIB_CB_FAST_CHECK = 8

CALIB_CB_FILTER_QUADS = 4

CALIB_CB_NORMALIZE_IMAGE = 2

CALIB_CB_SYMMETRIC_GRID = 1

CALIB_FIX_ASPECT_RATIO = 2

CALIB_FIX_FOCAL_LENGTH = 16

CALIB_FIX_INTRINSIC = 256
CALIB_FIX_K1 = 32
CALIB_FIX_K2 = 64
CALIB_FIX_K3 = 128
CALIB_FIX_K4 = 2048
CALIB_FIX_K5 = 4096
CALIB_FIX_K6 = 8192

CALIB_FIX_PRINCIPAL_POINT = 4

CALIB_FIX_S1_S2_S3_S4 = 65536

CALIB_FIX_TANGENT_DIST = 2097152

CALIB_FIX_TAUX_TAUY = 524288

CALIB_RATIONAL_MODEL = 16384

CALIB_SAME_FOCAL_LENGTH = 512

CALIB_THIN_PRISM_MODEL = 32768

CALIB_TILTED_MODEL = 262144

CALIB_USE_INTRINSIC_GUESS = 1

CALIB_USE_LU = 131072
CALIB_USE_QR = 1048576

CALIB_ZERO_DISPARITY = 1024

CALIB_ZERO_TANGENT_DIST = 8

CAP_ANDROID = 1000
CAP_ANY = 0
CAP_ARAVIS = 2100
CAP_AVFOUNDATION = 1200
CAP_CMU1394 = 300
CAP_DC1394 = 300
CAP_DSHOW = 700
CAP_FFMPEG = 1900
CAP_FIREWARE = 300
CAP_FIREWIRE = 300
CAP_GIGANETIX = 1300
CAP_GPHOTO2 = 1700
CAP_GSTREAMER = 1800
CAP_IEEE1394 = 300
CAP_IMAGES = 2000
CAP_INTELPERC = 1500

CAP_INTELPERC_DEPTH_GENERATOR = 536870912
CAP_INTELPERC_DEPTH_MAP = 0

CAP_INTELPERC_GENERATORS_MASK = 805306368

CAP_INTELPERC_IMAGE = 3

CAP_INTELPERC_IMAGE_GENERATOR = 268435456

CAP_INTELPERC_IR_MAP = 2

CAP_INTELPERC_UVDEPTH_MAP = 1

CAP_INTEL_MFX = 2300

CAP_MODE_BGR = 0
CAP_MODE_GRAY = 2
CAP_MODE_RGB = 1
CAP_MODE_YUYV = 3

CAP_MSMF = 1400

CAP_OPENCV_MJPEG = 2200

CAP_OPENNI = 900
CAP_OPENNI2 = 1600

CAP_OPENNI2_ASUS = 1610

CAP_OPENNI_ASUS = 910

CAP_OPENNI_BGR_IMAGE = 5

CAP_OPENNI_DEPTH_GENERATOR = -2147483648

CAP_OPENNI_DEPTH_GENERATOR_BASELINE = -2147483546

CAP_OPENNI_DEPTH_GENERATOR_FOCAL_LENGTH = -2147483545

CAP_OPENNI_DEPTH_GENERATOR_PRESENT = -2147483539
CAP_OPENNI_DEPTH_GENERATOR_REGISTRATION = -2147483544

CAP_OPENNI_DEPTH_GENERATOR_REGISTRATION_ON = -2147483544

CAP_OPENNI_DEPTH_MAP = 0

CAP_OPENNI_DISPARITY_MAP = 2

CAP_OPENNI_DISPARITY_MAP_32F = 3

CAP_OPENNI_GENERATORS_MASK = -536870912

CAP_OPENNI_GRAY_IMAGE = 6

CAP_OPENNI_IMAGE_GENERATOR = 1073741824

CAP_OPENNI_IMAGE_GENERATOR_OUTPUT_MODE = 1073741924

CAP_OPENNI_IMAGE_GENERATOR_PRESENT = 1073741933

CAP_OPENNI_IR_GENERATOR = 536870912

CAP_OPENNI_IR_GENERATOR_PRESENT = 536871021

CAP_OPENNI_IR_IMAGE = 7

CAP_OPENNI_POINT_CLOUD_MAP = 1

CAP_OPENNI_QVGA_30HZ = 3
CAP_OPENNI_QVGA_60HZ = 4

CAP_OPENNI_SXGA_15HZ = 1
CAP_OPENNI_SXGA_30HZ = 2

CAP_OPENNI_VALID_DEPTH_MASK = 4

CAP_OPENNI_VGA_30HZ = 0

CAP_PROP_APERTURE = 17008
CAP_PROP_AUTOFOCUS = 39

CAP_PROP_AUTO_EXPOSURE = 21

CAP_PROP_BACKLIGHT = 32
CAP_PROP_BRIGHTNESS = 10
CAP_PROP_BUFFERSIZE = 38
CAP_PROP_CONTRAST = 11

CAP_PROP_CONVERT_RGB = 16

CAP_PROP_DC1394_MAX = 31

CAP_PROP_DC1394_MODE_AUTO = -2
CAP_PROP_DC1394_MODE_MANUAL = -3

CAP_PROP_DC1394_MODE_ONE_PUSH_AUTO = -1

CAP_PROP_DC1394_OFF = -4

CAP_PROP_EXPOSURE = 15
CAP_PROP_EXPOSUREPROGRAM = 17009
CAP_PROP_FOCUS = 28
CAP_PROP_FORMAT = 8
CAP_PROP_FOURCC = 6
CAP_PROP_FPS = 5

CAP_PROP_FRAME_COUNT = 7
CAP_PROP_FRAME_HEIGHT = 4
CAP_PROP_FRAME_WIDTH = 3

CAP_PROP_GAIN = 14
CAP_PROP_GAMMA = 22

CAP_PROP_GIGA_FRAME_HEIGH_MAX = 10004

CAP_PROP_GIGA_FRAME_OFFSET_X = 10001
CAP_PROP_GIGA_FRAME_OFFSET_Y = 10002

CAP_PROP_GIGA_FRAME_SENS_HEIGH = 10006
CAP_PROP_GIGA_FRAME_SENS_WIDTH = 10005

CAP_PROP_GIGA_FRAME_WIDTH_MAX = 10003

CAP_PROP_GPHOTO2_COLLECT_MSGS = 17005

CAP_PROP_GPHOTO2_FLUSH_MSGS = 17006

CAP_PROP_GPHOTO2_PREVIEW = 17001

CAP_PROP_GPHOTO2_RELOAD_CONFIG = 17003

CAP_PROP_GPHOTO2_RELOAD_ON_CHANGE = 17004

CAP_PROP_GPHOTO2_WIDGET_ENUMERATE = 17002

CAP_PROP_GSTREAMER_QUEUE_LENGTH = 200

CAP_PROP_GUID = 29
CAP_PROP_HUE = 13

CAP_PROP_IMAGES_BASE = 18000
CAP_PROP_IMAGES_LAST = 19000

CAP_PROP_INTELPERC_DEPTH_CONFIDENCE_THRESHOLD = 11005

CAP_PROP_INTELPERC_DEPTH_FOCAL_LENGTH_HORZ = 11006
CAP_PROP_INTELPERC_DEPTH_FOCAL_LENGTH_VERT = 11007

CAP_PROP_INTELPERC_DEPTH_LOW_CONFIDENCE_VALUE = 11003

CAP_PROP_INTELPERC_DEPTH_SATURATION_VALUE = 11004

CAP_PROP_INTELPERC_PROFILE_COUNT = 11001
CAP_PROP_INTELPERC_PROFILE_IDX = 11002

CAP_PROP_IOS_DEVICE_EXPOSURE = 9002
CAP_PROP_IOS_DEVICE_FLASH = 9003
CAP_PROP_IOS_DEVICE_FOCUS = 9001
CAP_PROP_IOS_DEVICE_TORCH = 9005
CAP_PROP_IOS_DEVICE_WHITEBALANCE = 9004

CAP_PROP_IRIS = 36

CAP_PROP_ISO_SPEED = 30

CAP_PROP_MODE = 9
CAP_PROP_MONOCHROME = 19

CAP_PROP_OPENNI2_MIRROR = 111
CAP_PROP_OPENNI2_SYNC = 110

CAP_PROP_OPENNI_APPROX_FRAME_SYNC = 105

CAP_PROP_OPENNI_BASELINE = 102

CAP_PROP_OPENNI_CIRCLE_BUFFER = 107

CAP_PROP_OPENNI_FOCAL_LENGTH = 103

CAP_PROP_OPENNI_FRAME_MAX_DEPTH = 101

CAP_PROP_OPENNI_GENERATOR_PRESENT = 109

CAP_PROP_OPENNI_MAX_BUFFER_SIZE = 106

CAP_PROP_OPENNI_MAX_TIME_DURATION = 108

CAP_PROP_OPENNI_OUTPUT_MODE = 100

CAP_PROP_OPENNI_REGISTRATION = 104

CAP_PROP_OPENNI_REGISTRATION_ON = 104

CAP_PROP_PAN = 33

CAP_PROP_POS_AVI_RATIO = 2

CAP_PROP_POS_FRAMES = 1
CAP_PROP_POS_MSEC = 0

CAP_PROP_PVAPI_BINNINGX = 304
CAP_PROP_PVAPI_BINNINGY = 305
CAP_PROP_PVAPI_DECIMATIONHORIZONTAL = 302
CAP_PROP_PVAPI_DECIMATIONVERTICAL = 303
CAP_PROP_PVAPI_FRAMESTARTTRIGGERMODE = 301
CAP_PROP_PVAPI_MULTICASTIP = 300
CAP_PROP_PVAPI_PIXELFORMAT = 306

CAP_PROP_RECTIFICATION = 18
CAP_PROP_ROLL = 35
CAP_PROP_SATURATION = 12
CAP_PROP_SETTINGS = 37
CAP_PROP_SHARPNESS = 20
CAP_PROP_SPEED = 17007
CAP_PROP_TEMPERATURE = 23
CAP_PROP_TILT = 34
CAP_PROP_TRIGGER = 24

CAP_PROP_TRIGGER_DELAY = 25

CAP_PROP_VIEWFINDER = 17010

CAP_PROP_WHITE_BALANCE_BLUE_U = 17

CAP_PROP_WHITE_BALANCE_RED_V = 26

CAP_PROP_XI_ACQ_BUFFER_SIZE = 548

CAP_PROP_XI_ACQ_BUFFER_SIZE_UNIT = 549

CAP_PROP_XI_ACQ_FRAME_BURST_COUNT = 499

CAP_PROP_XI_ACQ_TIMING_MODE = 538

CAP_PROP_XI_ACQ_TRANSPORT_BUFFER_COMMIT = 552
CAP_PROP_XI_ACQ_TRANSPORT_BUFFER_SIZE = 550

CAP_PROP_XI_AEAG = 415

CAP_PROP_XI_AEAG_LEVEL = 419

CAP_PROP_XI_AEAG_ROI_HEIGHT = 442

CAP_PROP_XI_AEAG_ROI_OFFSET_X = 439
CAP_PROP_XI_AEAG_ROI_OFFSET_Y = 440

CAP_PROP_XI_AEAG_ROI_WIDTH = 441

CAP_PROP_XI_AE_MAX_LIMIT = 417

CAP_PROP_XI_AG_MAX_LIMIT = 418

CAP_PROP_XI_APPLY_CMS = 471

CAP_PROP_XI_AUTO_BANDWIDTH_CALCULATION = 573

CAP_PROP_XI_AUTO_WB = 414

CAP_PROP_XI_AVAILABLE_BANDWIDTH = 539

CAP_PROP_XI_BINNING_HORIZONTAL = 429
CAP_PROP_XI_BINNING_PATTERN = 430
CAP_PROP_XI_BINNING_SELECTOR = 427
CAP_PROP_XI_BINNING_VERTICAL = 428

CAP_PROP_XI_BPC = 445

CAP_PROP_XI_BUFFERS_QUEUE_SIZE = 551

CAP_PROP_XI_BUFFER_POLICY = 540

CAP_PROP_XI_CC_MATRIX_00 = 479
CAP_PROP_XI_CC_MATRIX_01 = 480
CAP_PROP_XI_CC_MATRIX_02 = 481
CAP_PROP_XI_CC_MATRIX_03 = 482
CAP_PROP_XI_CC_MATRIX_10 = 483
CAP_PROP_XI_CC_MATRIX_11 = 484
CAP_PROP_XI_CC_MATRIX_12 = 485
CAP_PROP_XI_CC_MATRIX_13 = 486
CAP_PROP_XI_CC_MATRIX_20 = 487
CAP_PROP_XI_CC_MATRIX_21 = 488
CAP_PROP_XI_CC_MATRIX_22 = 489
CAP_PROP_XI_CC_MATRIX_23 = 490
CAP_PROP_XI_CC_MATRIX_30 = 491
CAP_PROP_XI_CC_MATRIX_31 = 492
CAP_PROP_XI_CC_MATRIX_32 = 493
CAP_PROP_XI_CC_MATRIX_33 = 494

CAP_PROP_XI_CHIP_TEMP = 468

CAP_PROP_XI_CMS = 470

CAP_PROP_XI_COLOR_FILTER_ARRAY = 475

CAP_PROP_XI_COLUMN_FPN_CORRECTION = 555

CAP_PROP_XI_COOLING = 466

CAP_PROP_XI_COUNTER_SELECTOR = 536
CAP_PROP_XI_COUNTER_VALUE = 537

CAP_PROP_XI_DATA_FORMAT = 401

CAP_PROP_XI_DEBOUNCE_EN = 507
CAP_PROP_XI_DEBOUNCE_POL = 510
CAP_PROP_XI_DEBOUNCE_T0 = 508
CAP_PROP_XI_DEBOUNCE_T1 = 509

CAP_PROP_XI_DEBUG_LEVEL = 572

CAP_PROP_XI_DECIMATION_HORIZONTAL = 433
CAP_PROP_XI_DECIMATION_PATTERN = 434
CAP_PROP_XI_DECIMATION_SELECTOR = 431
CAP_PROP_XI_DECIMATION_VERTICAL = 432

CAP_PROP_XI_DEFAULT_CC_MATRIX = 495

CAP_PROP_XI_DEVICE_MODEL_ID = 521

CAP_PROP_XI_DEVICE_RESET = 554
CAP_PROP_XI_DEVICE_SN = 522

CAP_PROP_XI_DOWNSAMPLING = 400

CAP_PROP_XI_DOWNSAMPLING_TYPE = 426

CAP_PROP_XI_EXPOSURE = 421

CAP_PROP_XI_EXPOSURE_BURST_COUNT = 422

CAP_PROP_XI_EXP_PRIORITY = 416

CAP_PROP_XI_FFS_ACCESS_KEY = 583

CAP_PROP_XI_FFS_FILE_ID = 594
CAP_PROP_XI_FFS_FILE_SIZE = 580

CAP_PROP_XI_FRAMERATE = 535

CAP_PROP_XI_FREE_FFS_SIZE = 581

CAP_PROP_XI_GAIN = 424

CAP_PROP_XI_GAIN_SELECTOR = 423

CAP_PROP_XI_GAMMAC = 477
CAP_PROP_XI_GAMMAY = 476

CAP_PROP_XI_GPI_LEVEL = 408
CAP_PROP_XI_GPI_MODE = 407
CAP_PROP_XI_GPI_SELECTOR = 406

CAP_PROP_XI_GPO_MODE = 410
CAP_PROP_XI_GPO_SELECTOR = 409

CAP_PROP_XI_HDR = 559

CAP_PROP_XI_HDR_KNEEPOINT_COUNT = 560

CAP_PROP_XI_HDR_T1 = 561
CAP_PROP_XI_HDR_T2 = 562

CAP_PROP_XI_HEIGHT = 452

CAP_PROP_XI_HOUS_BACK_SIDE_TEMP = 590

CAP_PROP_XI_HOUS_TEMP = 469

CAP_PROP_XI_HW_REVISION = 571

CAP_PROP_XI_IMAGE_BLACK_LEVEL = 565

CAP_PROP_XI_IMAGE_DATA_BIT_DEPTH = 462

CAP_PROP_XI_IMAGE_DATA_FORMAT = 435

CAP_PROP_XI_IMAGE_DATA_FORMAT_RGB32_ALPHA = 529

CAP_PROP_XI_IMAGE_IS_COLOR = 474

CAP_PROP_XI_IMAGE_PAYLOAD_SIZE = 530

CAP_PROP_XI_IS_COOLED = 465

CAP_PROP_XI_IS_DEVICE_EXIST = 547

CAP_PROP_XI_KNEEPOINT1 = 563
CAP_PROP_XI_KNEEPOINT2 = 564

CAP_PROP_XI_LED_MODE = 412
CAP_PROP_XI_LED_SELECTOR = 411

CAP_PROP_XI_LENS_APERTURE_VALUE = 512

CAP_PROP_XI_LENS_FEATURE = 518

CAP_PROP_XI_LENS_FEATURE_SELECTOR = 517

CAP_PROP_XI_LENS_FOCAL_LENGTH = 516

CAP_PROP_XI_LENS_FOCUS_DISTANCE = 515
CAP_PROP_XI_LENS_FOCUS_MOVE = 514

CAP_PROP_XI_LENS_FOCUS_MOVEMENT_VALUE = 513

CAP_PROP_XI_LENS_MODE = 511

CAP_PROP_XI_LIMIT_BANDWIDTH = 459

CAP_PROP_XI_LUT_EN = 541
CAP_PROP_XI_LUT_INDEX = 542
CAP_PROP_XI_LUT_VALUE = 543

CAP_PROP_XI_MANUAL_WB = 413

CAP_PROP_XI_OFFSET_X = 402
CAP_PROP_XI_OFFSET_Y = 403

CAP_PROP_XI_OUTPUT_DATA_BIT_DEPTH = 461

CAP_PROP_XI_OUTPUT_DATA_PACKING = 463

CAP_PROP_XI_OUTPUT_DATA_PACKING_TYPE = 464

CAP_PROP_XI_RECENT_FRAME = 553

CAP_PROP_XI_REGION_MODE = 595
CAP_PROP_XI_REGION_SELECTOR = 589

CAP_PROP_XI_ROW_FPN_CORRECTION = 591

CAP_PROP_XI_SENSOR_BOARD_TEMP = 596

CAP_PROP_XI_SENSOR_CLOCK_FREQ_HZ = 532
CAP_PROP_XI_SENSOR_CLOCK_FREQ_INDEX = 533

CAP_PROP_XI_SENSOR_DATA_BIT_DEPTH = 460

CAP_PROP_XI_SENSOR_FEATURE_SELECTOR = 585
CAP_PROP_XI_SENSOR_FEATURE_VALUE = 586

CAP_PROP_XI_SENSOR_MODE = 558

CAP_PROP_XI_SENSOR_OUTPUT_CHANNEL_COUNT = 534

CAP_PROP_XI_SENSOR_TAPS = 437

CAP_PROP_XI_SHARPNESS = 478

CAP_PROP_XI_SHUTTER_TYPE = 436

CAP_PROP_XI_TARGET_TEMP = 467

CAP_PROP_XI_TEST_PATTERN = 588

CAP_PROP_XI_TEST_PATTERN_GENERATOR_SELECTOR = 587

CAP_PROP_XI_TIMEOUT = 420

CAP_PROP_XI_TRANSPORT_PIXEL_FORMAT = 531

CAP_PROP_XI_TRG_DELAY = 544
CAP_PROP_XI_TRG_SELECTOR = 498
CAP_PROP_XI_TRG_SOFTWARE = 405
CAP_PROP_XI_TRG_SOURCE = 404

CAP_PROP_XI_TS_RST_MODE = 545
CAP_PROP_XI_TS_RST_SOURCE = 546

CAP_PROP_XI_USED_FFS_SIZE = 582

CAP_PROP_XI_WB_KB = 450
CAP_PROP_XI_WB_KG = 449
CAP_PROP_XI_WB_KR = 448

CAP_PROP_XI_WIDTH = 451

CAP_PROP_ZOOM = 27

CAP_PVAPI = 800

CAP_PVAPI_DECIMATION_2OUTOF16 = 8
CAP_PVAPI_DECIMATION_2OUTOF4 = 2
CAP_PVAPI_DECIMATION_2OUTOF8 = 4
CAP_PVAPI_DECIMATION_OFF = 1

CAP_PVAPI_FSTRIGMODE_FIXEDRATE = 3
CAP_PVAPI_FSTRIGMODE_FREERUN = 0
CAP_PVAPI_FSTRIGMODE_SOFTWARE = 4
CAP_PVAPI_FSTRIGMODE_SYNCIN1 = 1
CAP_PVAPI_FSTRIGMODE_SYNCIN2 = 2

CAP_PVAPI_PIXELFORMAT_BAYER16 = 4
CAP_PVAPI_PIXELFORMAT_BAYER8 = 3
CAP_PVAPI_PIXELFORMAT_BGR24 = 6
CAP_PVAPI_PIXELFORMAT_BGRA32 = 8
CAP_PVAPI_PIXELFORMAT_MONO16 = 2
CAP_PVAPI_PIXELFORMAT_MONO8 = 1
CAP_PVAPI_PIXELFORMAT_RGB24 = 5
CAP_PVAPI_PIXELFORMAT_RGBA32 = 7

CAP_QT = 500
CAP_UNICAP = 600
CAP_V4L = 200
CAP_V4L2 = 200
CAP_VFW = 200
CAP_WINRT = 1410
CAP_XIAPI = 1100

CASCADE_DO_CANNY_PRUNING = 1

CASCADE_DO_ROUGH_SEARCH = 8

CASCADE_FIND_BIGGEST_OBJECT = 4

CASCADE_SCALE_IMAGE = 2

CCL_DEFAULT = -1
CCL_GRANA = 1
CCL_WU = 0

CC_STAT_AREA = 4
CC_STAT_HEIGHT = 3
CC_STAT_LEFT = 0
CC_STAT_MAX = 5
CC_STAT_TOP = 1
CC_STAT_WIDTH = 2

CHAIN_APPROX_NONE = 1
CHAIN_APPROX_SIMPLE = 2

CHAIN_APPROX_TC89_KCOS = 4
CHAIN_APPROX_TC89_L1 = 3

CirclesGridFinderParameters_ASYMMETRIC_GRID = 1

CirclesGridFinderParameters_SYMMETRIC_GRID = 0

CIRCLES_GRID_FINDER_PARAMETERS_ASYMMETRIC_GRID = 1

CIRCLES_GRID_FINDER_PARAMETERS_SYMMETRIC_GRID = 0

CMP_EQ = 0
CMP_GE = 2
CMP_GT = 1
CMP_LE = 4
CMP_LT = 3
CMP_NE = 5

COLORMAP_AUTUMN = 0
COLORMAP_BONE = 1
COLORMAP_COOL = 8
COLORMAP_HOT = 11
COLORMAP_HSV = 9
COLORMAP_JET = 2
COLORMAP_OCEAN = 5
COLORMAP_PARULA = 12
COLORMAP_PINK = 10
COLORMAP_RAINBOW = 4
COLORMAP_SPRING = 7
COLORMAP_SUMMER = 6
COLORMAP_WINTER = 3

COLOR_BayerBG2BGR = 46
COLOR_BayerBG2BGRA = 139

COLOR_BayerBG2BGR_EA = 135
COLOR_BayerBG2BGR_VNG = 62

COLOR_BayerBG2GRAY = 86
COLOR_BayerBG2RGB = 48
COLOR_BayerBG2RGBA = 141

COLOR_BayerBG2RGB_EA = 137
COLOR_BayerBG2RGB_VNG = 64

COLOR_BayerGB2BGR = 47
COLOR_BayerGB2BGRA = 140

COLOR_BayerGB2BGR_EA = 136
COLOR_BayerGB2BGR_VNG = 63

COLOR_BayerGB2GRAY = 87
COLOR_BayerGB2RGB = 49
COLOR_BayerGB2RGBA = 142

COLOR_BayerGB2RGB_EA = 138
COLOR_BayerGB2RGB_VNG = 65

COLOR_BayerGR2BGR = 49
COLOR_BayerGR2BGRA = 142

COLOR_BayerGR2BGR_EA = 138
COLOR_BayerGR2BGR_VNG = 65

COLOR_BayerGR2GRAY = 89
COLOR_BayerGR2RGB = 47
COLOR_BayerGR2RGBA = 140

COLOR_BayerGR2RGB_EA = 136
COLOR_BayerGR2RGB_VNG = 63

COLOR_BayerRG2BGR = 48
COLOR_BayerRG2BGRA = 141

COLOR_BayerRG2BGR_EA = 137
COLOR_BayerRG2BGR_VNG = 64

COLOR_BayerRG2GRAY = 88
COLOR_BayerRG2RGB = 46
COLOR_BayerRG2RGBA = 139

COLOR_BayerRG2RGB_EA = 135
COLOR_BayerRG2RGB_VNG = 62

COLOR_BAYER_BG2BGR = 46
COLOR_BAYER_BG2BGRA = 139

COLOR_BAYER_BG2BGR_EA = 135
COLOR_BAYER_BG2BGR_VNG = 62

COLOR_BAYER_BG2GRAY = 86
COLOR_BAYER_BG2RGB = 48
COLOR_BAYER_BG2RGBA = 141

COLOR_BAYER_BG2RGB_EA = 137
COLOR_BAYER_BG2RGB_VNG = 64

COLOR_BAYER_GB2BGR = 47
COLOR_BAYER_GB2BGRA = 140

COLOR_BAYER_GB2BGR_EA = 136
COLOR_BAYER_GB2BGR_VNG = 63

COLOR_BAYER_GB2GRAY = 87
COLOR_BAYER_GB2RGB = 49
COLOR_BAYER_GB2RGBA = 142

COLOR_BAYER_GB2RGB_EA = 138
COLOR_BAYER_GB2RGB_VNG = 65

COLOR_BAYER_GR2BGR = 49
COLOR_BAYER_GR2BGRA = 142

COLOR_BAYER_GR2BGR_EA = 138
COLOR_BAYER_GR2BGR_VNG = 65

COLOR_BAYER_GR2GRAY = 89
COLOR_BAYER_GR2RGB = 47
COLOR_BAYER_GR2RGBA = 140

COLOR_BAYER_GR2RGB_EA = 136
COLOR_BAYER_GR2RGB_VNG = 63

COLOR_BAYER_RG2BGR = 48
COLOR_BAYER_RG2BGRA = 141

COLOR_BAYER_RG2BGR_EA = 137
COLOR_BAYER_RG2BGR_VNG = 64

COLOR_BAYER_RG2GRAY = 88
COLOR_BAYER_RG2RGB = 46
COLOR_BAYER_RG2RGBA = 139

COLOR_BAYER_RG2RGB_EA = 135
COLOR_BAYER_RG2RGB_VNG = 62

COLOR_BGR2BGR555 = 22
COLOR_BGR2BGR565 = 12
COLOR_BGR2BGRA = 0
COLOR_BGR2GRAY = 6
COLOR_BGR2HLS = 52

COLOR_BGR2HLS_FULL = 68

COLOR_BGR2HSV = 40

COLOR_BGR2HSV_FULL = 66

COLOR_BGR2Lab = 44
COLOR_BGR2LAB = 44
COLOR_BGR2Luv = 50
COLOR_BGR2LUV = 50
COLOR_BGR2RGB = 4
COLOR_BGR2RGBA = 2
COLOR_BGR2XYZ = 32
COLOR_BGR2YCrCb = 36

COLOR_BGR2YCR_CB = 36

COLOR_BGR2YUV = 82

COLOR_BGR2YUV_I420 = 128
COLOR_BGR2YUV_IYUV = 128
COLOR_BGR2YUV_YV12 = 132

COLOR_BGR5552BGR = 24
COLOR_BGR5552BGRA = 28
COLOR_BGR5552GRAY = 31
COLOR_BGR5552RGB = 25
COLOR_BGR5552RGBA = 29
COLOR_BGR5652BGR = 14
COLOR_BGR5652BGRA = 18
COLOR_BGR5652GRAY = 21
COLOR_BGR5652RGB = 15
COLOR_BGR5652RGBA = 19
COLOR_BGRA2BGR = 1
COLOR_BGRA2BGR555 = 26
COLOR_BGRA2BGR565 = 16
COLOR_BGRA2GRAY = 10
COLOR_BGRA2RGB = 3
COLOR_BGRA2RGBA = 5

COLOR_BGRA2YUV_I420 = 130
COLOR_BGRA2YUV_IYUV = 130
COLOR_BGRA2YUV_YV12 = 134

COLOR_COLORCVT_MAX = 143

COLOR_GRAY2BGR = 8
COLOR_GRAY2BGR555 = 30
COLOR_GRAY2BGR565 = 20
COLOR_GRAY2BGRA = 9
COLOR_GRAY2RGB = 8
COLOR_GRAY2RGBA = 9
COLOR_HLS2BGR = 60

COLOR_HLS2BGR_FULL = 72

COLOR_HLS2RGB = 61

COLOR_HLS2RGB_FULL = 73

COLOR_HSV2BGR = 54

COLOR_HSV2BGR_FULL = 70

COLOR_HSV2RGB = 55

COLOR_HSV2RGB_FULL = 71

COLOR_Lab2BGR = 56
COLOR_LAB2BGR = 56
COLOR_Lab2LBGR = 78
COLOR_LAB2LBGR = 78
COLOR_Lab2LRGB = 79
COLOR_LAB2LRGB = 79
COLOR_Lab2RGB = 57
COLOR_LAB2RGB = 57
COLOR_LBGR2Lab = 74
COLOR_LBGR2LAB = 74
COLOR_LBGR2Luv = 76
COLOR_LBGR2LUV = 76
COLOR_LRGB2Lab = 75
COLOR_LRGB2LAB = 75
COLOR_LRGB2Luv = 77
COLOR_LRGB2LUV = 77
COLOR_Luv2BGR = 58
COLOR_LUV2BGR = 58
COLOR_Luv2LBGR = 80
COLOR_LUV2LBGR = 80
COLOR_Luv2LRGB = 81
COLOR_LUV2LRGB = 81
COLOR_Luv2RGB = 59
COLOR_LUV2RGB = 59
COLOR_mRGBA2RGBA = 126

COLOR_M_RGBA2RGBA = 126

COLOR_RGB2BGR = 4
COLOR_RGB2BGR555 = 23
COLOR_RGB2BGR565 = 13
COLOR_RGB2BGRA = 2
COLOR_RGB2GRAY = 7
COLOR_RGB2HLS = 53

COLOR_RGB2HLS_FULL = 69

COLOR_RGB2HSV = 41

COLOR_RGB2HSV_FULL = 67

COLOR_RGB2Lab = 45
COLOR_RGB2LAB = 45
COLOR_RGB2Luv = 51
COLOR_RGB2LUV = 51
COLOR_RGB2RGBA = 0
COLOR_RGB2XYZ = 33
COLOR_RGB2YCrCb = 37

COLOR_RGB2YCR_CB = 37

COLOR_RGB2YUV = 83

COLOR_RGB2YUV_I420 = 127
COLOR_RGB2YUV_IYUV = 127
COLOR_RGB2YUV_YV12 = 131

COLOR_RGBA2BGR = 3
COLOR_RGBA2BGR555 = 27
COLOR_RGBA2BGR565 = 17
COLOR_RGBA2BGRA = 5
COLOR_RGBA2GRAY = 11
COLOR_RGBA2mRGBA = 125

COLOR_RGBA2M_RGBA = 125

COLOR_RGBA2RGB = 1

COLOR_RGBA2YUV_I420 = 129
COLOR_RGBA2YUV_IYUV = 129
COLOR_RGBA2YUV_YV12 = 133

COLOR_XYZ2BGR = 34
COLOR_XYZ2RGB = 35
COLOR_YCrCb2BGR = 38
COLOR_YCrCb2RGB = 39

COLOR_YCR_CB2BGR = 38
COLOR_YCR_CB2RGB = 39

COLOR_YUV2BGR = 84

COLOR_YUV2BGRA_I420 = 105
COLOR_YUV2BGRA_IYUV = 105
COLOR_YUV2BGRA_NV12 = 95
COLOR_YUV2BGRA_NV21 = 97
COLOR_YUV2BGRA_UYNV = 112
COLOR_YUV2BGRA_UYVY = 112
COLOR_YUV2BGRA_Y422 = 112
COLOR_YUV2BGRA_YUNV = 120
COLOR_YUV2BGRA_YUY2 = 120
COLOR_YUV2BGRA_YUYV = 120
COLOR_YUV2BGRA_YV12 = 103
COLOR_YUV2BGRA_YVYU = 122

COLOR_YUV2BGR_I420 = 101
COLOR_YUV2BGR_IYUV = 101
COLOR_YUV2BGR_NV12 = 91
COLOR_YUV2BGR_NV21 = 93
COLOR_YUV2BGR_UYNV = 108
COLOR_YUV2BGR_UYVY = 108
COLOR_YUV2BGR_Y422 = 108
COLOR_YUV2BGR_YUNV = 116
COLOR_YUV2BGR_YUY2 = 116
COLOR_YUV2BGR_YUYV = 116
COLOR_YUV2BGR_YV12 = 99
COLOR_YUV2BGR_YVYU = 118

COLOR_YUV2GRAY_420 = 106
COLOR_YUV2GRAY_I420 = 106
COLOR_YUV2GRAY_IYUV = 106
COLOR_YUV2GRAY_NV12 = 106
COLOR_YUV2GRAY_NV21 = 106
COLOR_YUV2GRAY_UYNV = 123
COLOR_YUV2GRAY_UYVY = 123
COLOR_YUV2GRAY_Y422 = 123
COLOR_YUV2GRAY_YUNV = 124
COLOR_YUV2GRAY_YUY2 = 124
COLOR_YUV2GRAY_YUYV = 124
COLOR_YUV2GRAY_YV12 = 106
COLOR_YUV2GRAY_YVYU = 124

COLOR_YUV2RGB = 85

COLOR_YUV2RGBA_I420 = 104
COLOR_YUV2RGBA_IYUV = 104
COLOR_YUV2RGBA_NV12 = 94
COLOR_YUV2RGBA_NV21 = 96
COLOR_YUV2RGBA_UYNV = 111
COLOR_YUV2RGBA_UYVY = 111
COLOR_YUV2RGBA_Y422 = 111
COLOR_YUV2RGBA_YUNV = 119
COLOR_YUV2RGBA_YUY2 = 119
COLOR_YUV2RGBA_YUYV = 119
COLOR_YUV2RGBA_YV12 = 102
COLOR_YUV2RGBA_YVYU = 121

COLOR_YUV2RGB_I420 = 100
COLOR_YUV2RGB_IYUV = 100
COLOR_YUV2RGB_NV12 = 90
COLOR_YUV2RGB_NV21 = 92
COLOR_YUV2RGB_UYNV = 107
COLOR_YUV2RGB_UYVY = 107
COLOR_YUV2RGB_Y422 = 107
COLOR_YUV2RGB_YUNV = 115
COLOR_YUV2RGB_YUY2 = 115
COLOR_YUV2RGB_YUYV = 115
COLOR_YUV2RGB_YV12 = 98
COLOR_YUV2RGB_YVYU = 117

COLOR_YUV420p2BGR = 99
COLOR_YUV420P2BGR = 99
COLOR_YUV420p2BGRA = 103
COLOR_YUV420P2BGRA = 103
COLOR_YUV420p2GRAY = 106
COLOR_YUV420P2GRAY = 106
COLOR_YUV420p2RGB = 98
COLOR_YUV420P2RGB = 98
COLOR_YUV420p2RGBA = 102
COLOR_YUV420P2RGBA = 102
COLOR_YUV420sp2BGR = 93
COLOR_YUV420SP2BGR = 93
COLOR_YUV420sp2BGRA = 97
COLOR_YUV420SP2BGRA = 97
COLOR_YUV420sp2GRAY = 106
COLOR_YUV420SP2GRAY = 106
COLOR_YUV420sp2RGB = 92
COLOR_YUV420SP2RGB = 92
COLOR_YUV420sp2RGBA = 96
COLOR_YUV420SP2RGBA = 96

CONTOURS_MATCH_I1 = 1
CONTOURS_MATCH_I2 = 2
CONTOURS_MATCH_I3 = 3

COVAR_COLS = 16
COVAR_NORMAL = 1
COVAR_ROWS = 8
COVAR_SCALE = 4
COVAR_SCRAMBLED = 0

COVAR_USE_AVG = 2

CV_16S = 3
CV_16SC1 = 3
CV_16SC2 = 11
CV_16SC3 = 19
CV_16SC4 = 27
CV_16U = 2
CV_16UC1 = 2
CV_16UC2 = 10
CV_16UC3 = 18
CV_16UC4 = 26
CV_32F = 5
CV_32FC1 = 5
CV_32FC2 = 13
CV_32FC3 = 21
CV_32FC4 = 29
CV_32S = 4
CV_32SC1 = 4
CV_32SC2 = 12
CV_32SC3 = 20
CV_32SC4 = 28
CV_64F = 6
CV_64FC1 = 6
CV_64FC2 = 14
CV_64FC3 = 22
CV_64FC4 = 30
CV_8S = 1
CV_8SC1 = 1
CV_8SC2 = 9
CV_8SC3 = 17
CV_8SC4 = 25
CV_8U = 0
CV_8UC1 = 0
CV_8UC2 = 8
CV_8UC3 = 16
CV_8UC4 = 24

DCT_INVERSE = 1
DCT_ROWS = 4

DECOMP_CHOLESKY = 3
DECOMP_EIG = 2
DECOMP_LU = 0
DECOMP_NORMAL = 16
DECOMP_QR = 4
DECOMP_SVD = 1

DescriptorMatcher_BRUTEFORCE = 2

DescriptorMatcher_BRUTEFORCE_HAMMING = 4
DescriptorMatcher_BRUTEFORCE_HAMMINGLUT = 5
DescriptorMatcher_BRUTEFORCE_L1 = 3
DescriptorMatcher_BRUTEFORCE_SL2 = 6

DescriptorMatcher_FLANNBASED = 1

DESCRIPTOR_MATCHER_BRUTEFORCE = 2

DESCRIPTOR_MATCHER_BRUTEFORCE_HAMMING = 4
DESCRIPTOR_MATCHER_BRUTEFORCE_HAMMINGLUT = 5
DESCRIPTOR_MATCHER_BRUTEFORCE_L1 = 3
DESCRIPTOR_MATCHER_BRUTEFORCE_SL2 = 6

DESCRIPTOR_MATCHER_FLANNBASED = 1

DFT_COMPLEX_INPUT = 64
DFT_COMPLEX_OUTPUT = 16

DFT_INVERSE = 1

DFT_REAL_OUTPUT = 32

DFT_ROWS = 4
DFT_SCALE = 2

DIST_C = 3
DIST_FAIR = 5
DIST_HUBER = 7
DIST_L1 = 1
DIST_L12 = 4
DIST_L2 = 2

DIST_LABEL_CCOMP = 0
DIST_LABEL_PIXEL = 1

DIST_MASK_3 = 3
DIST_MASK_5 = 5
DIST_MASK_PRECISE = 0

DIST_USER = -1
DIST_WELSCH = 6

DrawMatchesFlags_DEFAULT = 0

DrawMatchesFlags_DRAW_OVER_OUTIMG = 1

DrawMatchesFlags_DRAW_RICH_KEYPOINTS = 4

DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS = 2

DRAW_MATCHES_FLAGS_DEFAULT = 0

DRAW_MATCHES_FLAGS_DRAW_OVER_OUTIMG = 1

DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS = 4

DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTS = 2

EVENT_FLAG_ALTKEY = 32
EVENT_FLAG_CTRLKEY = 8
EVENT_FLAG_LBUTTON = 1
EVENT_FLAG_MBUTTON = 4
EVENT_FLAG_RBUTTON = 2
EVENT_FLAG_SHIFTKEY = 16

EVENT_LBUTTONDBLCLK = 7
EVENT_LBUTTONDOWN = 1
EVENT_LBUTTONUP = 4
EVENT_MBUTTONDBLCLK = 9
EVENT_MBUTTONDOWN = 3
EVENT_MBUTTONUP = 6
EVENT_MOUSEHWHEEL = 11
EVENT_MOUSEMOVE = 0
EVENT_MOUSEWHEEL = 10
EVENT_RBUTTONDBLCLK = 8
EVENT_RBUTTONDOWN = 2
EVENT_RBUTTONUP = 5

FastFeatureDetector_FAST_N = 10002

FastFeatureDetector_NONMAX_SUPPRESSION = 10001

FastFeatureDetector_THRESHOLD = 10000

FastFeatureDetector_TYPE_5_8 = 0

FastFeatureDetector_TYPE_7_12 = 1

FastFeatureDetector_TYPE_9_16 = 2

FAST_FEATURE_DETECTOR_FAST_N = 10002

FAST_FEATURE_DETECTOR_NONMAX_SUPPRESSION = 10001

FAST_FEATURE_DETECTOR_THRESHOLD = 10000

FAST_FEATURE_DETECTOR_TYPE_5_8 = 0

FAST_FEATURE_DETECTOR_TYPE_7_12 = 1

FAST_FEATURE_DETECTOR_TYPE_9_16 = 2

FileNode_EMPTY = 32
FileNode_FLOAT = 2
FileNode_FLOW = 8
FileNode_INT = 1
FileNode_MAP = 6
FileNode_NAMED = 64
FileNode_NONE = 0
FileNode_REAL = 2
FileNode_REF = 4
FileNode_SEQ = 5
FileNode_STR = 3
FileNode_STRING = 3

FileNode_TYPE_MASK = 7

FileNode_USER = 16

FileStorage_APPEND = 2
FileStorage_BASE64 = 64

FileStorage_FORMAT_AUTO = 0
FileStorage_FORMAT_JSON = 24
FileStorage_FORMAT_MASK = 56
FileStorage_FORMAT_XML = 8
FileStorage_FORMAT_YAML = 16

FileStorage_INSIDE_MAP = 4

FileStorage_MEMORY = 4

FileStorage_NAME_EXPECTED = 2

FileStorage_READ = 0
FileStorage_UNDEFINED = 0

FileStorage_VALUE_EXPECTED = 1

FileStorage_WRITE = 1

FileStorage_WRITE_BASE64 = 65

FILE_NODE_EMPTY = 32
FILE_NODE_FLOAT = 2
FILE_NODE_FLOW = 8
FILE_NODE_INT = 1
FILE_NODE_MAP = 6
FILE_NODE_NAMED = 64
FILE_NODE_NONE = 0
FILE_NODE_REAL = 2
FILE_NODE_REF = 4
FILE_NODE_SEQ = 5
FILE_NODE_STR = 3
FILE_NODE_STRING = 3

FILE_NODE_TYPE_MASK = 7

FILE_NODE_USER = 16

FILE_STORAGE_APPEND = 2
FILE_STORAGE_BASE64 = 64

FILE_STORAGE_FORMAT_AUTO = 0
FILE_STORAGE_FORMAT_JSON = 24
FILE_STORAGE_FORMAT_MASK = 56
FILE_STORAGE_FORMAT_XML = 8
FILE_STORAGE_FORMAT_YAML = 16

FILE_STORAGE_INSIDE_MAP = 4

FILE_STORAGE_MEMORY = 4

FILE_STORAGE_NAME_EXPECTED = 2

FILE_STORAGE_READ = 0
FILE_STORAGE_UNDEFINED = 0

FILE_STORAGE_VALUE_EXPECTED = 1

FILE_STORAGE_WRITE = 1

FILE_STORAGE_WRITE_BASE64 = 65

FILLED = -1

FLOODFILL_FIXED_RANGE = 65536

FLOODFILL_MASK_ONLY = 131072

FM_7POINT = 1
FM_8POINT = 2
FM_LMEDS = 4
FM_RANSAC = 8

FONT_HERSHEY_COMPLEX = 3

FONT_HERSHEY_COMPLEX_SMALL = 5

FONT_HERSHEY_DUPLEX = 2
FONT_HERSHEY_PLAIN = 1

FONT_HERSHEY_SCRIPT_COMPLEX = 7
FONT_HERSHEY_SCRIPT_SIMPLEX = 6

FONT_HERSHEY_SIMPLEX = 0
FONT_HERSHEY_TRIPLEX = 4

FONT_ITALIC = 16

Formatter_FMT_C = 5

FORMATTER_FMT_C = 5

Formatter_FMT_CSV = 2

FORMATTER_FMT_CSV = 2

Formatter_FMT_DEFAULT = 0

FORMATTER_FMT_DEFAULT = 0

Formatter_FMT_MATLAB = 1

FORMATTER_FMT_MATLAB = 1

Formatter_FMT_NUMPY = 4

FORMATTER_FMT_NUMPY = 4

Formatter_FMT_PYTHON = 3

FORMATTER_FMT_PYTHON = 3

GC_BGD = 0
GC_EVAL = 2
GC_FGD = 1

GC_INIT_WITH_MASK = 1
GC_INIT_WITH_RECT = 0

GC_PR_BGD = 2
GC_PR_FGD = 3

GEMM_1_T = 1

GEMM_2_T = 2

GEMM_3_T = 4

Hamming_normType = 6

HAMMING_NORM_TYPE = 6

HISTCMP_BHATTACHARYYA = 3
HISTCMP_CHISQR = 1

HISTCMP_CHISQR_ALT = 4

HISTCMP_CORREL = 0
HISTCMP_HELLINGER = 3
HISTCMP_INTERSECT = 2

HISTCMP_KL_DIV = 5

HOGDescriptor_DEFAULT_NLEVELS = 64

HOGDESCRIPTOR_DEFAULT_NLEVELS = 64

HOGDescriptor_L2Hys = 0

HOGDESCRIPTOR_L2HYS = 0

HOUGH_GRADIENT = 3

HOUGH_MULTI_SCALE = 2

HOUGH_PROBABILISTIC = 1
HOUGH_STANDARD = 0

IMREAD_ANYCOLOR = 4
IMREAD_ANYDEPTH = 2
IMREAD_COLOR = 1
IMREAD_GRAYSCALE = 0

IMREAD_IGNORE_ORIENTATION = 128

IMREAD_LOAD_GDAL = 8

IMREAD_REDUCED_COLOR_2 = 17
IMREAD_REDUCED_COLOR_4 = 33
IMREAD_REDUCED_COLOR_8 = 65

IMREAD_REDUCED_GRAYSCALE_2 = 16
IMREAD_REDUCED_GRAYSCALE_4 = 32
IMREAD_REDUCED_GRAYSCALE_8 = 64

IMREAD_UNCHANGED = -1

IMWRITE_JPEG_CHROMA_QUALITY = 6

IMWRITE_JPEG_LUMA_QUALITY = 5

IMWRITE_JPEG_OPTIMIZE = 3
IMWRITE_JPEG_PROGRESSIVE = 2
IMWRITE_JPEG_QUALITY = 1

IMWRITE_JPEG_RST_INTERVAL = 4

IMWRITE_PAM_FORMAT_BLACKANDWHITE = 1
IMWRITE_PAM_FORMAT_GRAYSCALE = 2

IMWRITE_PAM_FORMAT_GRAYSCALE_ALPHA = 3

IMWRITE_PAM_FORMAT_NULL = 0
IMWRITE_PAM_FORMAT_RGB = 4

IMWRITE_PAM_FORMAT_RGB_ALPHA = 5

IMWRITE_PAM_TUPLETYPE = 128

IMWRITE_PNG_BILEVEL = 18
IMWRITE_PNG_COMPRESSION = 16
IMWRITE_PNG_STRATEGY = 17

IMWRITE_PNG_STRATEGY_DEFAULT = 0
IMWRITE_PNG_STRATEGY_FILTERED = 1
IMWRITE_PNG_STRATEGY_FIXED = 4

IMWRITE_PNG_STRATEGY_HUFFMAN_ONLY = 2

IMWRITE_PNG_STRATEGY_RLE = 3

IMWRITE_PXM_BINARY = 32

IMWRITE_WEBP_QUALITY = 64

INPAINT_NS = 0
INPAINT_TELEA = 1

INTERSECT_FULL = 2
INTERSECT_NONE = 0
INTERSECT_PARTIAL = 1

INTER_AREA = 3
INTER_BITS = 5
INTER_BITS2 = 10
INTER_CUBIC = 2
INTER_LANCZOS4 = 4
INTER_LINEAR = 1
INTER_MAX = 7
INTER_NEAREST = 0

INTER_TAB_SIZE = 32
INTER_TAB_SIZE2 = 1024

KAZE_DIFF_CHARBONNIER = 3

KAZE_DIFF_PM_G1 = 0
KAZE_DIFF_PM_G2 = 1

KAZE_DIFF_WEICKERT = 2

KMEANS_PP_CENTERS = 2

KMEANS_RANDOM_CENTERS = 0

KMEANS_USE_INITIAL_LABELS = 1

LDR_SIZE = 256

LINE_4 = 4
LINE_8 = 8
LINE_AA = 16

LMEDS = 4

LSD_REFINE_ADV = 2
LSD_REFINE_NONE = 0
LSD_REFINE_STD = 1

MARKER_CROSS = 0
MARKER_DIAMOND = 3
MARKER_SQUARE = 4
MARKER_STAR = 2

MARKER_TILTED_CROSS = 1

MARKER_TRIANGLE_DOWN = 6
MARKER_TRIANGLE_UP = 5

Mat_AUTO_STEP = 0

MAT_AUTO_STEP = 0

Mat_CONTINUOUS_FLAG = 16384

MAT_CONTINUOUS_FLAG = 16384

Mat_DEPTH_MASK = 7

MAT_DEPTH_MASK = 7

Mat_MAGIC_MASK = -65536

MAT_MAGIC_MASK = -65536

Mat_MAGIC_VAL = 1124007936

MAT_MAGIC_VAL = 1124007936

Mat_SUBMATRIX_FLAG = 32768

MAT_SUBMATRIX_FLAG = 32768

Mat_TYPE_MASK = 4095

MAT_TYPE_MASK = 4095

MIXED_CLONE = 2

MONOCHROME_TRANSFER = 3

MORPH_BLACKHAT = 6
MORPH_CLOSE = 3
MORPH_CROSS = 1
MORPH_DILATE = 1
MORPH_ELLIPSE = 2
MORPH_ERODE = 0
MORPH_GRADIENT = 4
MORPH_HITMISS = 7
MORPH_OPEN = 2
MORPH_RECT = 0
MORPH_TOPHAT = 5

MOTION_AFFINE = 2
MOTION_EUCLIDEAN = 1
MOTION_HOMOGRAPHY = 3
MOTION_TRANSLATION = 0

NORMAL_CLONE = 1

NORMCONV_FILTER = 2

NORM_HAMMING = 6
NORM_HAMMING2 = 7
NORM_INF = 1
NORM_L1 = 2
NORM_L2 = 4
NORM_L2SQR = 5
NORM_MINMAX = 32
NORM_RELATIVE = 8

NORM_TYPE_MASK = 7

OPTFLOW_FARNEBACK_GAUSSIAN = 256

OPTFLOW_LK_GET_MIN_EIGENVALS = 8

OPTFLOW_USE_INITIAL_FLOW = 4

ORB_FAST_SCORE = 1

ORB_HARRIS_SCORE = 0

ORB_kBytes = 32

ORB_K_BYTES = 32

Param_ALGORITHM = 6

PARAM_ALGORITHM = 6

Param_BOOLEAN = 1

PARAM_BOOLEAN = 1

Param_FLOAT = 7

PARAM_FLOAT = 7

Param_INT = 0

PARAM_INT = 0

Param_MAT = 4

PARAM_MAT = 4

Param_MAT_VECTOR = 5

PARAM_MAT_VECTOR = 5

Param_REAL = 2

PARAM_REAL = 2

Param_STRING = 3

PARAM_STRING = 3

Param_UCHAR = 11

PARAM_UCHAR = 11

Param_UINT64 = 9

PARAM_UINT64 = 9

Param_UNSIGNED_INT = 8

PARAM_UNSIGNED_INT = 8

PCA_DATA_AS_COL = 1
PCA_DATA_AS_ROW = 0

PCA_USE_AVG = 2

PROJ_SPHERICAL_EQRECT = 1
PROJ_SPHERICAL_ORTHO = 0

QT_CHECKBOX = 1

QT_FONT_BLACK = 87
QT_FONT_BOLD = 75
QT_FONT_DEMIBOLD = 63
QT_FONT_LIGHT = 25
QT_FONT_NORMAL = 50

QT_NEW_BUTTONBAR = 1024

QT_PUSH_BUTTON = 0

QT_RADIOBOX = 2

QT_STYLE_ITALIC = 1
QT_STYLE_NORMAL = 0
QT_STYLE_OBLIQUE = 2

RANSAC = 8

RECURS_FILTER = 1

REDUCE_AVG = 1
REDUCE_MAX = 2
REDUCE_MIN = 3
REDUCE_SUM = 0

RETR_CCOMP = 2
RETR_EXTERNAL = 0
RETR_FLOODFILL = 4
RETR_LIST = 1
RETR_TREE = 3

RHO = 16

RNG_NORMAL = 1
RNG_UNIFORM = 0

ROTATE_180 = 1

ROTATE_90_CLOCKWISE = 0
ROTATE_90_COUNTERCLOCKWISE = 2

SOLVELP_MULTI = 1
SOLVELP_SINGLE = 0
SOLVELP_UNBOUNDED = -2
SOLVELP_UNFEASIBLE = -1

SOLVEPNP_AP3P = 5
SOLVEPNP_DLS = 3
SOLVEPNP_EPNP = 1
SOLVEPNP_ITERATIVE = 0

SOLVEPNP_MAX_COUNT = 6

SOLVEPNP_P3P = 2
SOLVEPNP_UPNP = 4

SORT_ASCENDING = 0
SORT_DESCENDING = 16

SORT_EVERY_COLUMN = 1
SORT_EVERY_ROW = 0

SparseMat_HASH_BIT = -2147483648
SparseMat_HASH_SCALE = 1540483477

SparseMat_MAGIC_VAL = 1123876864

SparseMat_MAX_DIM = 32

SPARSE_MAT_HASH_BIT = -2147483648
SPARSE_MAT_HASH_SCALE = 1540483477

SPARSE_MAT_MAGIC_VAL = 1123876864

SPARSE_MAT_MAX_DIM = 32

StereoBM_PREFILTER_NORMALIZED_RESPONSE = 0

StereoBM_PREFILTER_XSOBEL = 1

StereoMatcher_DISP_SCALE = 16
StereoMatcher_DISP_SHIFT = 4

StereoSGBM_MODE_HH = 1
StereoSGBM_MODE_HH4 = 3
StereoSGBM_MODE_SGBM = 0

StereoSGBM_MODE_SGBM_3WAY = 2

STEREO_BM_PREFILTER_NORMALIZED_RESPONSE = 0

STEREO_BM_PREFILTER_XSOBEL = 1

STEREO_MATCHER_DISP_SCALE = 16
STEREO_MATCHER_DISP_SHIFT = 4

STEREO_SGBM_MODE_HH = 1
STEREO_SGBM_MODE_HH4 = 3
STEREO_SGBM_MODE_SGBM = 0

STEREO_SGBM_MODE_SGBM_3WAY = 2

Stitcher_ERR_CAMERA_PARAMS_ADJUST_FAIL = 3

STITCHER_ERR_CAMERA_PARAMS_ADJUST_FAIL = 3

Stitcher_ERR_HOMOGRAPHY_EST_FAIL = 2

STITCHER_ERR_HOMOGRAPHY_EST_FAIL = 2

Stitcher_ERR_NEED_MORE_IMGS = 1

STITCHER_ERR_NEED_MORE_IMGS = 1

Stitcher_OK = 0

STITCHER_OK = 0

Stitcher_ORIG_RESOL = -1

STITCHER_ORIG_RESOL = -1

Stitcher_PANORAMA = 0

STITCHER_PANORAMA = 0

Stitcher_SCANS = 1

STITCHER_SCANS = 1

Subdiv2D_NEXT_AROUND_DST = 34

SUBDIV2D_NEXT_AROUND_DST = 34

Subdiv2D_NEXT_AROUND_LEFT = 19

SUBDIV2D_NEXT_AROUND_LEFT = 19

Subdiv2D_NEXT_AROUND_ORG = 0

SUBDIV2D_NEXT_AROUND_ORG = 0

Subdiv2D_NEXT_AROUND_RIGHT = 49

SUBDIV2D_NEXT_AROUND_RIGHT = 49

Subdiv2D_PREV_AROUND_DST = 51

SUBDIV2D_PREV_AROUND_DST = 51

Subdiv2D_PREV_AROUND_LEFT = 32

SUBDIV2D_PREV_AROUND_LEFT = 32

Subdiv2D_PREV_AROUND_ORG = 17

SUBDIV2D_PREV_AROUND_ORG = 17

Subdiv2D_PREV_AROUND_RIGHT = 2

SUBDIV2D_PREV_AROUND_RIGHT = 2

Subdiv2D_PTLOC_ERROR = -2

SUBDIV2D_PTLOC_ERROR = -2

Subdiv2D_PTLOC_INSIDE = 0

SUBDIV2D_PTLOC_INSIDE = 0

Subdiv2D_PTLOC_ON_EDGE = 2

SUBDIV2D_PTLOC_ON_EDGE = 2

Subdiv2D_PTLOC_OUTSIDE_RECT = -1

SUBDIV2D_PTLOC_OUTSIDE_RECT = -1

Subdiv2D_PTLOC_VERTEX = 1

SUBDIV2D_PTLOC_VERTEX = 1

SVD_FULL_UV = 4

SVD_MODIFY_A = 1

SVD_NO_UV = 2

TermCriteria_COUNT = 1
TermCriteria_EPS = 2

TermCriteria_MAX_ITER = 1

TERM_CRITERIA_COUNT = 1
TERM_CRITERIA_EPS = 2

TERM_CRITERIA_MAX_ITER = 1

THRESH_BINARY = 0

THRESH_BINARY_INV = 1

THRESH_MASK = 7
THRESH_OTSU = 8
THRESH_TOZERO = 3

THRESH_TOZERO_INV = 4

THRESH_TRIANGLE = 16
THRESH_TRUNC = 2

TM_CCOEFF = 4

TM_CCOEFF_NORMED = 5

TM_CCORR = 2

TM_CCORR_NORMED = 3

TM_SQDIFF = 0

TM_SQDIFF_NORMED = 1

UMatData_ASYNC_CLEANUP = 128

UMatData_COPY_ON_MAP = 1

UMatData_DEVICE_COPY_OBSOLETE = 4

UMatData_DEVICE_MEM_MAPPED = 64

UMatData_HOST_COPY_OBSOLETE = 2

UMatData_TEMP_COPIED_UMAT = 24

UMatData_TEMP_UMAT = 8

UMatData_USER_ALLOCATED = 32

UMat_AUTO_STEP = 0

UMAT_AUTO_STEP = 0

UMat_CONTINUOUS_FLAG = 16384

UMAT_CONTINUOUS_FLAG = 16384

UMAT_DATA_ASYNC_CLEANUP = 128

UMAT_DATA_COPY_ON_MAP = 1

UMAT_DATA_DEVICE_COPY_OBSOLETE = 4

UMAT_DATA_DEVICE_MEM_MAPPED = 64

UMAT_DATA_HOST_COPY_OBSOLETE = 2

UMAT_DATA_TEMP_COPIED_UMAT = 24

UMAT_DATA_TEMP_UMAT = 8

UMAT_DATA_USER_ALLOCATED = 32

UMat_DEPTH_MASK = 7

UMAT_DEPTH_MASK = 7

UMat_MAGIC_MASK = -65536

UMAT_MAGIC_MASK = -65536

UMat_MAGIC_VAL = 1124007936

UMAT_MAGIC_VAL = 1124007936

UMat_SUBMATRIX_FLAG = 32768

UMAT_SUBMATRIX_FLAG = 32768

UMat_TYPE_MASK = 4095

UMAT_TYPE_MASK = 4095

USAGE_ALLOCATE_DEVICE_MEMORY = 2

USAGE_ALLOCATE_HOST_MEMORY = 1

USAGE_ALLOCATE_SHARED_MEMORY = 4

USAGE_DEFAULT = 0

VIDEOWRITER_PROP_FRAMEBYTES = 2
VIDEOWRITER_PROP_NSTRIPES = 3
VIDEOWRITER_PROP_QUALITY = 1

WARP_FILL_OUTLIERS = 8

WARP_INVERSE_MAP = 16

WINDOW_AUTOSIZE = 1
WINDOW_FREERATIO = 256
WINDOW_FULLSCREEN = 1

WINDOW_GUI_EXPANDED = 0
WINDOW_GUI_NORMAL = 16

WINDOW_KEEPRATIO = 0
WINDOW_NORMAL = 0
WINDOW_OPENGL = 4096

WND_PROP_ASPECT_RATIO = 2

WND_PROP_AUTOSIZE = 1
WND_PROP_FULLSCREEN = 0
WND_PROP_OPENGL = 3
WND_PROP_VISIBLE = 4

_InputArray_CUDA_GPU_MAT = 589824

_InputArray_CUDA_HOST_MEM = 524288

_InputArray_EXPR = 393216

_InputArray_FIXED_SIZE = 1073741824
_InputArray_FIXED_TYPE = -2147483648

_InputArray_KIND_MASK = 2031616
_InputArray_KIND_SHIFT = 16

_InputArray_MAT = 65536
_InputArray_MATX = 131072
_InputArray_NONE = 0

_InputArray_OPENGL_BUFFER = 458752

_InputArray_STD_ARRAY = 917504

_InputArray_STD_ARRAY_MAT = 983040

_InputArray_STD_BOOL_VECTOR = 786432

_InputArray_STD_VECTOR = 196608

_InputArray_STD_VECTOR_CUDA_GPU_MAT = 851968

_InputArray_STD_VECTOR_MAT = 327680
_InputArray_STD_VECTOR_UMAT = 720896
_InputArray_STD_VECTOR_VECTOR = 262144

_InputArray_UMAT = 655360

_INPUT_ARRAY_CUDA_GPU_MAT = 589824

_INPUT_ARRAY_CUDA_HOST_MEM = 524288

_INPUT_ARRAY_EXPR = 393216

_INPUT_ARRAY_FIXED_SIZE = 1073741824
_INPUT_ARRAY_FIXED_TYPE = -2147483648

_INPUT_ARRAY_KIND_MASK = 2031616
_INPUT_ARRAY_KIND_SHIFT = 16

_INPUT_ARRAY_MAT = 65536
_INPUT_ARRAY_MATX = 131072
_INPUT_ARRAY_NONE = 0

_INPUT_ARRAY_OPENGL_BUFFER = 458752

_INPUT_ARRAY_STD_ARRAY = 917504

_INPUT_ARRAY_STD_ARRAY_MAT = 983040

_INPUT_ARRAY_STD_BOOL_VECTOR = 786432

_INPUT_ARRAY_STD_VECTOR = 196608

_INPUT_ARRAY_STD_VECTOR_CUDA_GPU_MAT = 851968

_INPUT_ARRAY_STD_VECTOR_MAT = 327680
_INPUT_ARRAY_STD_VECTOR_UMAT = 720896
_INPUT_ARRAY_STD_VECTOR_VECTOR = 262144

_INPUT_ARRAY_UMAT = 655360

_OutputArray_DEPTH_MASK_16S = 8
_OutputArray_DEPTH_MASK_16U = 4
_OutputArray_DEPTH_MASK_32F = 32
_OutputArray_DEPTH_MASK_32S = 16
_OutputArray_DEPTH_MASK_64F = 64
_OutputArray_DEPTH_MASK_8S = 2
_OutputArray_DEPTH_MASK_8U = 1
_OutputArray_DEPTH_MASK_ALL = 127

_OutputArray_DEPTH_MASK_ALL_BUT_8S = 125

_OutputArray_DEPTH_MASK_FLT = 96

_OUTPUT_ARRAY_DEPTH_MASK_16S = 8
_OUTPUT_ARRAY_DEPTH_MASK_16U = 4
_OUTPUT_ARRAY_DEPTH_MASK_32F = 32
_OUTPUT_ARRAY_DEPTH_MASK_32S = 16
_OUTPUT_ARRAY_DEPTH_MASK_64F = 64
_OUTPUT_ARRAY_DEPTH_MASK_8S = 2
_OUTPUT_ARRAY_DEPTH_MASK_8U = 1
_OUTPUT_ARRAY_DEPTH_MASK_ALL = 127

_OUTPUT_ARRAY_DEPTH_MASK_ALL_BUT_8S = 125

_OUTPUT_ARRAY_DEPTH_MASK_FLT = 96

__UMAT_USAGE_FLAGS_32BIT = 2147483647

__version__ = '3.3.0'

# functions

def absdiff(src1, src2, dst=None): # real signature unknown; restored from __doc__
    """
    absdiff(src1, src2[, dst]) -> dst
    .   @brief Calculates the per-element absolute difference between two arrays or between an array and a scalar.
    .   
    .   The function cv::absdiff calculates:
    .   *   Absolute difference between two arrays when they have the same
    .   size and type:
    .   \f[\texttt{dst}(I) =  \texttt{saturate} (| \texttt{src1}(I) -  \texttt{src2}(I)|)\f]
    .   *   Absolute difference between an array and a scalar when the second
    .   array is constructed from Scalar or has as many elements as the
    .   number of channels in `src1`:
    .   \f[\texttt{dst}(I) =  \texttt{saturate} (| \texttt{src1}(I) -  \texttt{src2} |)\f]
    .   *   Absolute difference between a scalar and an array when the first
    .   array is constructed from Scalar or has as many elements as the
    .   number of channels in `src2`:
    .   \f[\texttt{dst}(I) =  \texttt{saturate} (| \texttt{src1} -  \texttt{src2}(I) |)\f]
    .   where I is a multi-dimensional index of array elements. In case of
    .   multi-channel arrays, each channel is processed independently.
    .   @note Saturation is not applied when the arrays have the depth CV_32S.
    .   You may even get a negative value in the case of overflow.
    .   @param src1 first input array or a scalar.
    .   @param src2 second input array or a scalar.
    .   @param dst output array that has the same size and type as input arrays.
    .   @sa cv::abs(const Mat&)
    """
    pass

def accumulate(src, dst, mask=None): # real signature unknown; restored from __doc__
    """
    accumulate(src, dst[, mask]) -> dst
    .   @brief Adds an image to the accumulator.
    .   
    .   The function adds src or some of its elements to dst :
    .   
    .   \f[\texttt{dst} (x,y)  \leftarrow \texttt{dst} (x,y) +  \texttt{src} (x,y)  \quad \text{if} \quad \texttt{mask} (x,y)  \ne 0\f]
    .   
    .   The function supports multi-channel images. Each channel is processed independently.
    .   
    .   The functions accumulate\* can be used, for example, to collect statistics of a scene background
    .   viewed by a still camera and for the further foreground-background segmentation.
    .   
    .   @param src Input image of type CV_8UC(n), CV_16UC(n), CV_32FC(n) or CV_64FC(n), where n is a positive integer.
    .   @param dst %Accumulator image with the same number of channels as input image, and a depth of CV_32F or CV_64F.
    .   @param mask Optional operation mask.
    .   
    .   @sa  accumulateSquare, accumulateProduct, accumulateWeighted
    """
    pass

def accumulateProduct(src1, src2, dst, mask=None): # real signature unknown; restored from __doc__
    """
    accumulateProduct(src1, src2, dst[, mask]) -> dst
    .   @brief Adds the per-element product of two input images to the accumulator.
    .   
    .   The function adds the product of two images or their selected regions to the accumulator dst :
    .   
    .   \f[\texttt{dst} (x,y)  \leftarrow \texttt{dst} (x,y) +  \texttt{src1} (x,y)  \cdot \texttt{src2} (x,y)  \quad \text{if} \quad \texttt{mask} (x,y)  \ne 0\f]
    .   
    .   The function supports multi-channel images. Each channel is processed independently.
    .   
    .   @param src1 First input image, 1- or 3-channel, 8-bit or 32-bit floating point.
    .   @param src2 Second input image of the same type and the same size as src1 .
    .   @param dst %Accumulator with the same number of channels as input images, 32-bit or 64-bit
    .   floating-point.
    .   @param mask Optional operation mask.
    .   
    .   @sa  accumulate, accumulateSquare, accumulateWeighted
    """
    pass

def accumulateSquare(src, dst, mask=None): # real signature unknown; restored from __doc__
    """
    accumulateSquare(src, dst[, mask]) -> dst
    .   @brief Adds the square of a source image to the accumulator.
    .   
    .   The function adds the input image src or its selected region, raised to a power of 2, to the
    .   accumulator dst :
    .   
    .   \f[\texttt{dst} (x,y)  \leftarrow \texttt{dst} (x,y) +  \texttt{src} (x,y)^2  \quad \text{if} \quad \texttt{mask} (x,y)  \ne 0\f]
    .   
    .   The function supports multi-channel images. Each channel is processed independently.
    .   
    .   @param src Input image as 1- or 3-channel, 8-bit or 32-bit floating point.
    .   @param dst %Accumulator image with the same number of channels as input image, 32-bit or 64-bit
    .   floating-point.
    .   @param mask Optional operation mask.
    .   
    .   @sa  accumulateSquare, accumulateProduct, accumulateWeighted
    """
    pass

def accumulateWeighted(src, dst, alpha, mask=None): # real signature unknown; restored from __doc__
    """
    accumulateWeighted(src, dst, alpha[, mask]) -> dst
    .   @brief Updates a running average.
    .   
    .   The function calculates the weighted sum of the input image src and the accumulator dst so that dst
    .   becomes a running average of a frame sequence:
    .   
    .   \f[\texttt{dst} (x,y)  \leftarrow (1- \texttt{alpha} )  \cdot \texttt{dst} (x,y) +  \texttt{alpha} \cdot \texttt{src} (x,y)  \quad \text{if} \quad \texttt{mask} (x,y)  \ne 0\f]
    .   
    .   That is, alpha regulates the update speed (how fast the accumulator "forgets" about earlier images).
    .   The function supports multi-channel images. Each channel is processed independently.
    .   
    .   @param src Input image as 1- or 3-channel, 8-bit or 32-bit floating point.
    .   @param dst %Accumulator image with the same number of channels as input image, 32-bit or 64-bit
    .   floating-point.
    .   @param alpha Weight of the input image.
    .   @param mask Optional operation mask.
    .   
    .   @sa  accumulate, accumulateSquare, accumulateProduct
    """
    pass

def adaptiveThreshold(src, maxValue, adaptiveMethod, thresholdType, blockSize, C, dst=None): # real signature unknown; restored from __doc__
    """
    adaptiveThreshold(src, maxValue, adaptiveMethod, thresholdType, blockSize, C[, dst]) -> dst
    .   @brief Applies an adaptive threshold to an array.
    .   
    .   The function transforms a grayscale image to a binary image according to the formulae:
    .   -   **THRESH_BINARY**
    .   \f[dst(x,y) =  \fork{\texttt{maxValue}}{if \(src(x,y) > T(x,y)\)}{0}{otherwise}\f]
    .   -   **THRESH_BINARY_INV**
    .   \f[dst(x,y) =  \fork{0}{if \(src(x,y) > T(x,y)\)}{\texttt{maxValue}}{otherwise}\f]
    .   where \f$T(x,y)\f$ is a threshold calculated individually for each pixel (see adaptiveMethod parameter).
    .   
    .   The function can process the image in-place.
    .   
    .   @param src Source 8-bit single-channel image.
    .   @param dst Destination image of the same size and the same type as src.
    .   @param maxValue Non-zero value assigned to the pixels for which the condition is satisfied
    .   @param adaptiveMethod Adaptive thresholding algorithm to use, see cv::AdaptiveThresholdTypes
    .   @param thresholdType Thresholding type that must be either THRESH_BINARY or THRESH_BINARY_INV,
    .   see cv::ThresholdTypes.
    .   @param blockSize Size of a pixel neighborhood that is used to calculate a threshold value for the
    .   pixel: 3, 5, 7, and so on.
    .   @param C Constant subtracted from the mean or weighted mean (see the details below). Normally, it
    .   is positive but may be zero or negative as well.
    .   
    .   @sa  threshold, blur, GaussianBlur
    """
    pass

def add(src1, src2, dst=None, mask=None, dtype=None): # real signature unknown; restored from __doc__
    """
    add(src1, src2[, dst[, mask[, dtype]]]) -> dst
    .   @brief Calculates the per-element sum of two arrays or an array and a scalar.
    .   
    .   The function add calculates:
    .   - Sum of two arrays when both input arrays have the same size and the same number of channels:
    .   \f[\texttt{dst}(I) =  \texttt{saturate} ( \texttt{src1}(I) +  \texttt{src2}(I)) \quad \texttt{if mask}(I) \ne0\f]
    .   - Sum of an array and a scalar when src2 is constructed from Scalar or has the same number of
    .   elements as `src1.channels()`:
    .   \f[\texttt{dst}(I) =  \texttt{saturate} ( \texttt{src1}(I) +  \texttt{src2} ) \quad \texttt{if mask}(I) \ne0\f]
    .   - Sum of a scalar and an array when src1 is constructed from Scalar or has the same number of
    .   elements as `src2.channels()`:
    .   \f[\texttt{dst}(I) =  \texttt{saturate} ( \texttt{src1} +  \texttt{src2}(I) ) \quad \texttt{if mask}(I) \ne0\f]
    .   where `I` is a multi-dimensional index of array elements. In case of multi-channel arrays, each
    .   channel is processed independently.
    .   
    .   The first function in the list above can be replaced with matrix expressions:
    .   @code{.cpp}
    .   dst = src1 + src2;
    .   dst += src1; // equivalent to add(dst, src1, dst);
    .   @endcode
    .   The input arrays and the output array can all have the same or different depths. For example, you
    .   can add a 16-bit unsigned array to a 8-bit signed array and store the sum as a 32-bit
    .   floating-point array. Depth of the output array is determined by the dtype parameter. In the second
    .   and third cases above, as well as in the first case, when src1.depth() == src2.depth(), dtype can
    .   be set to the default -1. In this case, the output array will have the same depth as the input
    .   array, be it src1, src2 or both.
    .   @note Saturation is not applied when the output array has the depth CV_32S. You may even get
    .   result of an incorrect sign in the case of overflow.
    .   @param src1 first input array or a scalar.
    .   @param src2 second input array or a scalar.
    .   @param dst output array that has the same size and number of channels as the input array(s); the
    .   depth is defined by dtype or src1/src2.
    .   @param mask optional operation mask - 8-bit single channel array, that specifies elements of the
    .   output array to be changed.
    .   @param dtype optional depth of the output array (see the discussion below).
    .   @sa subtract, addWeighted, scaleAdd, Mat::convertTo
    """
    pass

def addText(img, text, org, nameFont, pointSize=None, color=None, weight=None, style=None, spacing=None): # real signature unknown; restored from __doc__
    """
    addText(img, text, org, nameFont[, pointSize[, color[, weight[, style[, spacing]]]]]) -> None
    .   @brief Draws a text on the image.
    .   
    .   @param img 8-bit 3-channel image where the text should be drawn.
    .   @param text Text to write on an image.
    .   @param org Point(x,y) where the text should start on an image.
    .   @param nameFont Name of the font. The name should match the name of a system font (such as
    .   *Times*). If the font is not found, a default one is used.
    .   @param pointSize Size of the font. If not specified, equal zero or negative, the point size of the
    .   font is set to a system-dependent default value. Generally, this is 12 points.
    .   @param color Color of the font in BGRA where A = 255 is fully transparent.
    .   @param weight Font weight. Available operation flags are : cv::QtFontWeights You can also specify a positive integer for better control.
    .   @param style Font style. Available operation flags are : cv::QtFontStyles
    .   @param spacing Spacing between characters. It can be negative or positive.
    """
    pass

def addWeighted(src1, alpha, src2, beta, gamma, dst=None, dtype=None): # real signature unknown; restored from __doc__
    """
    addWeighted(src1, alpha, src2, beta, gamma[, dst[, dtype]]) -> dst
    .   @brief Calculates the weighted sum of two arrays.
    .   
    .   The function addWeighted calculates the weighted sum of two arrays as follows:
    .   \f[\texttt{dst} (I)= \texttt{saturate} ( \texttt{src1} (I)* \texttt{alpha} +  \texttt{src2} (I)* \texttt{beta} +  \texttt{gamma} )\f]
    .   where I is a multi-dimensional index of array elements. In case of multi-channel arrays, each
    .   channel is processed independently.
    .   The function can be replaced with a matrix expression:
    .   @code{.cpp}
    .   dst = src1*alpha + src2*beta + gamma;
    .   @endcode
    .   @note Saturation is not applied when the output array has the depth CV_32S. You may even get
    .   result of an incorrect sign in the case of overflow.
    .   @param src1 first input array.
    .   @param alpha weight of the first array elements.
    .   @param src2 second input array of the same size and channel number as src1.
    .   @param beta weight of the second array elements.
    .   @param gamma scalar added to each sum.
    .   @param dst output array that has the same size and number of channels as the input arrays.
    .   @param dtype optional depth of the output array; when both input arrays have the same depth, dtype
    .   can be set to -1, which will be equivalent to src1.depth().
    .   @sa  add, subtract, scaleAdd, Mat::convertTo
    """
    pass

def AgastFeatureDetector_create(threshold=None, nonmaxSuppression=None, type=None): # real signature unknown; restored from __doc__
    """
    AgastFeatureDetector_create([, threshold[, nonmaxSuppression[, type]]]) -> retval
    .
    """
    pass

def AKAZE_create(descriptor_type=None, descriptor_size=None, descriptor_channels=None, threshold=None, nOctaves=None, nOctaveLayers=None, diffusivity=None): # real signature unknown; restored from __doc__
    """
    AKAZE_create([, descriptor_type[, descriptor_size[, descriptor_channels[, threshold[, nOctaves[, nOctaveLayers[, diffusivity]]]]]]]) -> retval
    .   @brief The AKAZE constructor
    .   
    .   @param descriptor_type Type of the extracted descriptor: DESCRIPTOR_KAZE,
    .   DESCRIPTOR_KAZE_UPRIGHT, DESCRIPTOR_MLDB or DESCRIPTOR_MLDB_UPRIGHT.
    .   @param descriptor_size Size of the descriptor in bits. 0 -\> Full size
    .   @param descriptor_channels Number of channels in the descriptor (1, 2, 3)
    .   @param threshold Detector response threshold to accept point
    .   @param nOctaves Maximum octave evolution of the image
    .   @param nOctaveLayers Default number of sublevels per scale level
    .   @param diffusivity Diffusivity type. DIFF_PM_G1, DIFF_PM_G2, DIFF_WEICKERT or
    .   DIFF_CHARBONNIER
    """
    pass

def applyColorMap(src, colormap, dst=None): # real signature unknown; restored from __doc__
    """
    applyColorMap(src, colormap[, dst]) -> dst
    .   @brief Applies a GNU Octave/MATLAB equivalent colormap on a given image.
    .   
    .   @param src The source image, grayscale or colored of type CV_8UC1 or CV_8UC3.
    .   @param dst The result is the colormapped source image. Note: Mat::create is called on dst.
    .   @param colormap The colormap to apply, see cv::ColormapTypes
    
    
    
    applyColorMap(src, userColor[, dst]) -> dst
    .   @brief Applies a user colormap on a given image.
    .   
    .   @param src The source image, grayscale or colored of type CV_8UC1 or CV_8UC3.
    .   @param dst The result is the colormapped source image. Note: Mat::create is called on dst.
    .   @param userColor The colormap to apply of type CV_8UC1 or CV_8UC3 and size 256
    """
    pass

def approxPolyDP(curve, epsilon, closed, approxCurve=None): # real signature unknown; restored from __doc__
    """
    approxPolyDP(curve, epsilon, closed[, approxCurve]) -> approxCurve
    .   @brief Approximates a polygonal curve(s) with the specified precision.
    .   
    .   The function cv::approxPolyDP approximates a curve or a polygon with another curve/polygon with less
    .   vertices so that the distance between them is less or equal to the specified precision. It uses the
    .   Douglas-Peucker algorithm <http://en.wikipedia.org/wiki/Ramer-Douglas-Peucker_algorithm>
    .   
    .   @param curve Input vector of a 2D point stored in std::vector or Mat
    .   @param approxCurve Result of the approximation. The type should match the type of the input curve.
    .   @param epsilon Parameter specifying the approximation accuracy. This is the maximum distance
    .   between the original curve and its approximation.
    .   @param closed If true, the approximated curve is closed (its first and last vertices are
    .   connected). Otherwise, it is not closed.
    """
    pass

def arcLength(curve, closed): # real signature unknown; restored from __doc__
    """
    arcLength(curve, closed) -> retval
    .   @brief Calculates a contour perimeter or a curve length.
    .   
    .   The function computes a curve length or a closed contour perimeter.
    .   
    .   @param curve Input vector of 2D points, stored in std::vector or Mat.
    .   @param closed Flag indicating whether the curve is closed or not.
    """
    pass

def arrowedLine(img, pt1, pt2, color, thickness=None, line_type=None, shift=None, tipLength=None): # real signature unknown; restored from __doc__
    """
    arrowedLine(img, pt1, pt2, color[, thickness[, line_type[, shift[, tipLength]]]]) -> img
    .   @brief Draws a arrow segment pointing from the first point to the second one.
    .   
    .   The function arrowedLine draws an arrow between pt1 and pt2 points in the image. See also cv::line.
    .   
    .   @param img Image.
    .   @param pt1 The point the arrow starts from.
    .   @param pt2 The point the arrow points to.
    .   @param color Line color.
    .   @param thickness Line thickness.
    .   @param line_type Type of the line, see cv::LineTypes
    .   @param shift Number of fractional bits in the point coordinates.
    .   @param tipLength The length of the arrow tip in relation to the arrow length
    """
    pass

def batchDistance(src1, src2, dtype, dist=None, nidx=None, normType=None, K=None, mask=None, update=None, crosscheck=None): # real signature unknown; restored from __doc__
    """
    batchDistance(src1, src2, dtype[, dist[, nidx[, normType[, K[, mask[, update[, crosscheck]]]]]]]) -> dist, nidx
    .   @brief naive nearest neighbor finder
    .   
    .   see http://en.wikipedia.org/wiki/Nearest_neighbor_search
    .   @todo document
    """
    pass

def BFMatcher(normType=None, crossCheck=None): # real signature unknown; restored from __doc__
    """
    BFMatcher([, normType[, crossCheck]]) -> <BFMatcher object>
    .   @brief Brute-force matcher constructor (obsolete). Please use BFMatcher.create()
    .   *
    .   *
    """
    pass

def BFMatcher_create(normType=None, crossCheck=None): # real signature unknown; restored from __doc__
    """
    BFMatcher_create([, normType[, crossCheck]]) -> retval
    .
    """
    pass

def bilateralFilter(src, d, sigmaColor, sigmaSpace, dst=None, borderType=None): # real signature unknown; restored from __doc__
    """
    bilateralFilter(src, d, sigmaColor, sigmaSpace[, dst[, borderType]]) -> dst
    .   @brief Applies the bilateral filter to an image.
    .   
    .   The function applies bilateral filtering to the input image, as described in
    .   http://www.dai.ed.ac.uk/CVonline/LOCAL_COPIES/MANDUCHI1/Bilateral_Filtering.html
    .   bilateralFilter can reduce unwanted noise very well while keeping edges fairly sharp. However, it is
    .   very slow compared to most filters.
    .   
    .   _Sigma values_: For simplicity, you can set the 2 sigma values to be the same. If they are small (\<
    .   10), the filter will not have much effect, whereas if they are large (\> 150), they will have a very
    .   strong effect, making the image look "cartoonish".
    .   
    .   _Filter size_: Large filters (d \> 5) are very slow, so it is recommended to use d=5 for real-time
    .   applications, and perhaps d=9 for offline applications that need heavy noise filtering.
    .   
    .   This filter does not work inplace.
    .   @param src Source 8-bit or floating-point, 1-channel or 3-channel image.
    .   @param dst Destination image of the same size and type as src .
    .   @param d Diameter of each pixel neighborhood that is used during filtering. If it is non-positive,
    .   it is computed from sigmaSpace.
    .   @param sigmaColor Filter sigma in the color space. A larger value of the parameter means that
    .   farther colors within the pixel neighborhood (see sigmaSpace) will be mixed together, resulting
    .   in larger areas of semi-equal color.
    .   @param sigmaSpace Filter sigma in the coordinate space. A larger value of the parameter means that
    .   farther pixels will influence each other as long as their colors are close enough (see sigmaColor
    .   ). When d\>0, it specifies the neighborhood size regardless of sigmaSpace. Otherwise, d is
    .   proportional to sigmaSpace.
    .   @param borderType border mode used to extrapolate pixels outside of the image, see cv::BorderTypes
    """
    pass

def bitwise_and(src1, src2, dst=None, mask=None): # real signature unknown; restored from __doc__
    """
    bitwise_and(src1, src2[, dst[, mask]]) -> dst
    .   @brief computes bitwise conjunction of the two arrays (dst = src1 & src2)
    .   Calculates the per-element bit-wise conjunction of two arrays or an
    .   array and a scalar.
    .   
    .   The function cv::bitwise_and calculates the per-element bit-wise logical conjunction for:
    .   *   Two arrays when src1 and src2 have the same size:
    .   \f[\texttt{dst} (I) =  \texttt{src1} (I)  \wedge \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f]
    .   *   An array and a scalar when src2 is constructed from Scalar or has
    .   the same number of elements as `src1.channels()`:
    .   \f[\texttt{dst} (I) =  \texttt{src1} (I)  \wedge \texttt{src2} \quad \texttt{if mask} (I) \ne0\f]
    .   *   A scalar and an array when src1 is constructed from Scalar or has
    .   the same number of elements as `src2.channels()`:
    .   \f[\texttt{dst} (I) =  \texttt{src1}  \wedge \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f]
    .   In case of floating-point arrays, their machine-specific bit
    .   representations (usually IEEE754-compliant) are used for the operation.
    .   In case of multi-channel arrays, each channel is processed
    .   independently. In the second and third cases above, the scalar is first
    .   converted to the array type.
    .   @param src1 first input array or a scalar.
    .   @param src2 second input array or a scalar.
    .   @param dst output array that has the same size and type as the input
    .   arrays.
    .   @param mask optional operation mask, 8-bit single channel array, that
    .   specifies elements of the output array to be changed.
    """
    pass

def bitwise_not(src, dst=None, mask=None): # real signature unknown; restored from __doc__
    """
    bitwise_not(src[, dst[, mask]]) -> dst
    .   @brief  Inverts every bit of an array.
    .   
    .   The function cv::bitwise_not calculates per-element bit-wise inversion of the input
    .   array:
    .   \f[\texttt{dst} (I) =  \neg \texttt{src} (I)\f]
    .   In case of a floating-point input array, its machine-specific bit
    .   representation (usually IEEE754-compliant) is used for the operation. In
    .   case of multi-channel arrays, each channel is processed independently.
    .   @param src input array.
    .   @param dst output array that has the same size and type as the input
    .   array.
    .   @param mask optional operation mask, 8-bit single channel array, that
    .   specifies elements of the output array to be changed.
    """
    pass

def bitwise_or(src1, src2, dst=None, mask=None): # real signature unknown; restored from __doc__
    """
    bitwise_or(src1, src2[, dst[, mask]]) -> dst
    .   @brief Calculates the per-element bit-wise disjunction of two arrays or an
    .   array and a scalar.
    .   
    .   The function cv::bitwise_or calculates the per-element bit-wise logical disjunction for:
    .   *   Two arrays when src1 and src2 have the same size:
    .   \f[\texttt{dst} (I) =  \texttt{src1} (I)  \vee \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f]
    .   *   An array and a scalar when src2 is constructed from Scalar or has
    .   the same number of elements as `src1.channels()`:
    .   \f[\texttt{dst} (I) =  \texttt{src1} (I)  \vee \texttt{src2} \quad \texttt{if mask} (I) \ne0\f]
    .   *   A scalar and an array when src1 is constructed from Scalar or has
    .   the same number of elements as `src2.channels()`:
    .   \f[\texttt{dst} (I) =  \texttt{src1}  \vee \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f]
    .   In case of floating-point arrays, their machine-specific bit
    .   representations (usually IEEE754-compliant) are used for the operation.
    .   In case of multi-channel arrays, each channel is processed
    .   independently. In the second and third cases above, the scalar is first
    .   converted to the array type.
    .   @param src1 first input array or a scalar.
    .   @param src2 second input array or a scalar.
    .   @param dst output array that has the same size and type as the input
    .   arrays.
    .   @param mask optional operation mask, 8-bit single channel array, that
    .   specifies elements of the output array to be changed.
    """
    pass

def bitwise_xor(src1, src2, dst=None, mask=None): # real signature unknown; restored from __doc__
    """
    bitwise_xor(src1, src2[, dst[, mask]]) -> dst
    .   @brief Calculates the per-element bit-wise "exclusive or" operation on two
    .   arrays or an array and a scalar.
    .   
    .   The function cv::bitwise_xor calculates the per-element bit-wise logical "exclusive-or"
    .   operation for:
    .   *   Two arrays when src1 and src2 have the same size:
    .   \f[\texttt{dst} (I) =  \texttt{src1} (I)  \oplus \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f]
    .   *   An array and a scalar when src2 is constructed from Scalar or has
    .   the same number of elements as `src1.channels()`:
    .   \f[\texttt{dst} (I) =  \texttt{src1} (I)  \oplus \texttt{src2} \quad \texttt{if mask} (I) \ne0\f]
    .   *   A scalar and an array when src1 is constructed from Scalar or has
    .   the same number of elements as `src2.channels()`:
    .   \f[\texttt{dst} (I) =  \texttt{src1}  \oplus \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\f]
    .   In case of floating-point arrays, their machine-specific bit
    .   representations (usually IEEE754-compliant) are used for the operation.
    .   In case of multi-channel arrays, each channel is processed
    .   independently. In the 2nd and 3rd cases above, the scalar is first
    .   converted to the array type.
    .   @param src1 first input array or a scalar.
    .   @param src2 second input array or a scalar.
    .   @param dst output array that has the same size and type as the input
    .   arrays.
    .   @param mask optional operation mask, 8-bit single channel array, that
    .   specifies elements of the output array to be changed.
    """
    pass

def blur(src, ksize, dst=None, anchor=None, borderType=None): # real signature unknown; restored from __doc__
    """
    blur(src, ksize[, dst[, anchor[, borderType]]]) -> dst
    .   @brief Blurs an image using the normalized box filter.
    .   
    .   The function smooths an image using the kernel:
    .   
    .   \f[\texttt{K} =  \frac{1}{\texttt{ksize.width*ksize.height}} \begin{bmatrix} 1 & 1 & 1 &  \cdots & 1 & 1  \\ 1 & 1 & 1 &  \cdots & 1 & 1  \\ \hdotsfor{6} \\ 1 & 1 & 1 &  \cdots & 1 & 1  \\ \end{bmatrix}\f]
    .   
    .   The call `blur(src, dst, ksize, anchor, borderType)` is equivalent to `boxFilter(src, dst, src.type(),
    .   anchor, true, borderType)`.
    .   
    .   @param src input image; it can have any number of channels, which are processed independently, but
    .   the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
    .   @param dst output image of the same size and type as src.
    .   @param ksize blurring kernel size.
    .   @param anchor anchor point; default value Point(-1,-1) means that the anchor is at the kernel
    .   center.
    .   @param borderType border mode used to extrapolate pixels outside of the image, see cv::BorderTypes
    .   @sa  boxFilter, bilateralFilter, GaussianBlur, medianBlur
    """
    pass

def borderInterpolate(p, len, borderType): # real signature unknown; restored from __doc__
    """
    borderInterpolate(p, len, borderType) -> retval
    .   @brief Computes the source location of an extrapolated pixel.
    .   
    .   The function computes and returns the coordinate of a donor pixel corresponding to the specified
    .   extrapolated pixel when using the specified extrapolation border mode. For example, if you use
    .   cv::BORDER_WRAP mode in the horizontal direction, cv::BORDER_REFLECT_101 in the vertical direction and
    .   want to compute value of the "virtual" pixel Point(-5, 100) in a floating-point image img , it
    .   looks like:
    .   @code{.cpp}
    .   float val = img.at<float>(borderInterpolate(100, img.rows, cv::BORDER_REFLECT_101),
    .   borderInterpolate(-5, img.cols, cv::BORDER_WRAP));
    .   @endcode
    .   Normally, the function is not called directly. It is used inside filtering functions and also in
    .   copyMakeBorder.
    .   @param p 0-based coordinate of the extrapolated pixel along one of the axes, likely \<0 or \>= len
    .   @param len Length of the array along the corresponding axis.
    .   @param borderType Border type, one of the cv::BorderTypes, except for cv::BORDER_TRANSPARENT and
    .   cv::BORDER_ISOLATED . When borderType==cv::BORDER_CONSTANT , the function always returns -1, regardless
    .   of p and len.
    .   
    .   @sa copyMakeBorder
    """
    pass

def boundingRect(points): # real signature unknown; restored from __doc__
    """
    boundingRect(points) -> retval
    .   @brief Calculates the up-right bounding rectangle of a point set.
    .   
    .   The function calculates and returns the minimal up-right bounding rectangle for the specified point set.
    .   
    .   @param points Input 2D point set, stored in std::vector or Mat.
    """
    pass

def BOWImgDescriptorExtractor(dextractor, dmatcher): # real signature unknown; restored from __doc__
    """
    BOWImgDescriptorExtractor(dextractor, dmatcher) -> <BOWImgDescriptorExtractor object>
    .   @brief The constructor.
    .   
    .   @param dextractor Descriptor extractor that is used to compute descriptors for an input image and
    .   its keypoints.
    .   @param dmatcher Descriptor matcher that is used to find the nearest word of the trained vocabulary
    .   for each keypoint descriptor of the image.
    """
    pass

def BOWKMeansTrainer(clusterCount, termcrit=None, attempts=None, flags=None): # real signature unknown; restored from __doc__
    """
    BOWKMeansTrainer(clusterCount[, termcrit[, attempts[, flags]]]) -> <BOWKMeansTrainer object>
    .   @brief The constructor.
    .   
    .   @see cv::kmeans
    """
    pass

def boxFilter(src, ddepth, ksize, dst=None, anchor=None, normalize=None, borderType=None): # real signature unknown; restored from __doc__
    """
    boxFilter(src, ddepth, ksize[, dst[, anchor[, normalize[, borderType]]]]) -> dst
    .   @brief Blurs an image using the box filter.
    .   
    .   The function smooths an image using the kernel:
    .   
    .   \f[\texttt{K} =  \alpha \begin{bmatrix} 1 & 1 & 1 &  \cdots & 1 & 1  \\ 1 & 1 & 1 &  \cdots & 1 & 1  \\ \hdotsfor{6} \\ 1 & 1 & 1 &  \cdots & 1 & 1 \end{bmatrix}\f]
    .   
    .   where
    .   
    .   \f[\alpha = \fork{\frac{1}{\texttt{ksize.width*ksize.height}}}{when \texttt{normalize=true}}{1}{otherwise}\f]
    .   
    .   Unnormalized box filter is useful for computing various integral characteristics over each pixel
    .   neighborhood, such as covariance matrices of image derivatives (used in dense optical flow
    .   algorithms, and so on). If you need to compute pixel sums over variable-size windows, use cv::integral.
    .   
    .   @param src input image.
    .   @param dst output image of the same size and type as src.
    .   @param ddepth the output image depth (-1 to use src.depth()).
    .   @param ksize blurring kernel size.
    .   @param anchor anchor point; default value Point(-1,-1) means that the anchor is at the kernel
    .   center.
    .   @param normalize flag, specifying whether the kernel is normalized by its area or not.
    .   @param borderType border mode used to extrapolate pixels outside of the image, see cv::BorderTypes
    .   @sa  blur, bilateralFilter, GaussianBlur, medianBlur, integral
    """
    pass

def boxPoints(box, points=None): # real signature unknown; restored from __doc__
    """
    boxPoints(box[, points]) -> points
    .   @brief Finds the four vertices of a rotated rect. Useful to draw the rotated rectangle.
    .   
    .   The function finds the four vertices of a rotated rectangle. This function is useful to draw the
    .   rectangle. In C++, instead of using this function, you can directly use box.points() method. Please
    .   visit the [tutorial on bounding
    .   rectangle](http://docs.opencv.org/doc/tutorials/imgproc/shapedescriptors/bounding_rects_circles/bounding_rects_circles.html#bounding-rects-circles)
    .   for more information.
    .   
    .   @param box The input rotated rectangle. It may be the output of
    .   @param points The output array of four vertices of rectangles.
    """
    pass

def BRISK_create(thresh=None, octaves=None, patternScale=None): # real signature unknown; restored from __doc__
    """
    BRISK_create([, thresh[, octaves[, patternScale]]]) -> retval
    .   @brief The BRISK constructor
    .   
    .   @param thresh AGAST detection threshold score.
    .   @param octaves detection octaves. Use 0 to do single scale.
    .   @param patternScale apply this scale to the pattern used for sampling the neighbourhood of a
    .   keypoint.
    
    
    
    BRISK_create(radiusList, numberList[, dMax[, dMin[, indexChange]]]) -> retval
    .   @brief The BRISK constructor for a custom pattern
    .   
    .   @param radiusList defines the radii (in pixels) where the samples around a keypoint are taken (for
    .   keypoint scale 1).
    .   @param numberList defines the number of sampling points on the sampling circle. Must be the same
    .   size as radiusList..
    .   @param dMax threshold for the short pairings used for descriptor formation (in pixels for keypoint
    .   scale 1).
    .   @param dMin threshold for the long pairings used for orientation determination (in pixels for
    .   keypoint scale 1).
    .   @param indexChange index remapping of the bits.
    """
    pass

def buildOpticalFlowPyramid(img, winSize, maxLevel, pyramid=None, withDerivatives=None, pyrBorder=None, derivBorder=None, tryReuseInputImage=None): # real signature unknown; restored from __doc__
    """
    buildOpticalFlowPyramid(img, winSize, maxLevel[, pyramid[, withDerivatives[, pyrBorder[, derivBorder[, tryReuseInputImage]]]]]) -> retval, pyramid
    .   @brief Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.
    .   
    .   @param img 8-bit input image.
    .   @param pyramid output pyramid.
    .   @param winSize window size of optical flow algorithm. Must be not less than winSize argument of
    .   calcOpticalFlowPyrLK. It is needed to calculate required padding for pyramid levels.
    .   @param maxLevel 0-based maximal pyramid level number.
    .   @param withDerivatives set to precompute gradients for the every pyramid level. If pyramid is
    .   constructed without the gradients then calcOpticalFlowPyrLK will calculate them internally.
    .   @param pyrBorder the border mode for pyramid layers.
    .   @param derivBorder the border mode for gradients.
    .   @param tryReuseInputImage put ROI of input image into the pyramid if possible. You can pass false
    .   to force data copying.
    .   @return number of levels in constructed pyramid. Can be less than maxLevel.
    """
    pass

def calcBackProject(images, channels, hist, ranges, scale, dst=None): # real signature unknown; restored from __doc__
    """
    calcBackProject(images, channels, hist, ranges, scale[, dst]) -> dst
    .   @overload
    """
    pass

def calcCovarMatrix(samples, mean, flags, covar=None, ctype=None): # real signature unknown; restored from __doc__
    """
    calcCovarMatrix(samples, mean, flags[, covar[, ctype]]) -> covar, mean
    .   @overload
    .   @note use cv::COVAR_ROWS or cv::COVAR_COLS flag
    .   @param samples samples stored as rows/columns of a single matrix.
    .   @param covar output covariance matrix of the type ctype and square size.
    .   @param mean input or output (depending on the flags) array as the average value of the input vectors.
    .   @param flags operation flags as a combination of cv::CovarFlags
    .   @param ctype type of the matrixl; it equals 'CV_64F' by default.
    """
    pass

def calcHist(images, channels, mask, histSize, ranges, hist=None, accumulate=None): # real signature unknown; restored from __doc__
    """
    calcHist(images, channels, mask, histSize, ranges[, hist[, accumulate]]) -> hist
    .   @overload
    """
    pass

def calcOpticalFlowFarneback(prev, next, flow, pyr_scale, levels, winsize, iterations, poly_n, poly_sigma, flags): # real signature unknown; restored from __doc__
    """
    calcOpticalFlowFarneback(prev, next, flow, pyr_scale, levels, winsize, iterations, poly_n, poly_sigma, flags) -> flow
    .   @brief Computes a dense optical flow using the Gunnar Farneback's algorithm.
    .   
    .   @param prev first 8-bit single-channel input image.
    .   @param next second input image of the same size and the same type as prev.
    .   @param flow computed flow image that has the same size as prev and type CV_32FC2.
    .   @param pyr_scale parameter, specifying the image scale (\<1) to build pyramids for each image;
    .   pyr_scale=0.5 means a classical pyramid, where each next layer is twice smaller than the previous
    .   one.
    .   @param levels number of pyramid layers including the initial image; levels=1 means that no extra
    .   layers are created and only the original images are used.
    .   @param winsize averaging window size; larger values increase the algorithm robustness to image
    .   noise and give more chances for fast motion detection, but yield more blurred motion field.
    .   @param iterations number of iterations the algorithm does at each pyramid level.
    .   @param poly_n size of the pixel neighborhood used to find polynomial expansion in each pixel;
    .   larger values mean that the image will be approximated with smoother surfaces, yielding more
    .   robust algorithm and more blurred motion field, typically poly_n =5 or 7.
    .   @param poly_sigma standard deviation of the Gaussian that is used to smooth derivatives used as a
    .   basis for the polynomial expansion; for poly_n=5, you can set poly_sigma=1.1, for poly_n=7, a
    .   good value would be poly_sigma=1.5.
    .   @param flags operation flags that can be a combination of the following:
    .   -   **OPTFLOW_USE_INITIAL_FLOW** uses the input flow as an initial flow approximation.
    .   -   **OPTFLOW_FARNEBACK_GAUSSIAN** uses the Gaussian \f$\texttt{winsize}\times\texttt{winsize}\f$
    .   filter instead of a box filter of the same size for optical flow estimation; usually, this
    .   option gives z more accurate flow than with a box filter, at the cost of lower speed;
    .   normally, winsize for a Gaussian window should be set to a larger value to achieve the same
    .   level of robustness.
    .   
    .   The function finds an optical flow for each prev pixel using the @cite Farneback2003 algorithm so that
    .   
    .   \f[\texttt{prev} (y,x)  \sim \texttt{next} ( y + \texttt{flow} (y,x)[1],  x + \texttt{flow} (y,x)[0])\f]
    .   
    .   @note
    .   
    .   -   An example using the optical flow algorithm described by Gunnar Farneback can be found at
    .   opencv_source_code/samples/cpp/fback.cpp
    .   -   (Python) An example using the optical flow algorithm described by Gunnar Farneback can be
    .   found at opencv_source_code/samples/python/opt_flow.py
    """
    pass

def calcOpticalFlowPyrLK(prevImg, nextImg, prevPts, nextPts, status=None, err=None, winSize=None, maxLevel=None, criteria=None, flags=None, minEigThreshold=None): # real signature unknown; restored from __doc__
    """
    calcOpticalFlowPyrLK(prevImg, nextImg, prevPts, nextPts[, status[, err[, winSize[, maxLevel[, criteria[, flags[, minEigThreshold]]]]]]]) -> nextPts, status, err
    .   @brief Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade method with
    .   pyramids.
    .   
    .   @param prevImg first 8-bit input image or pyramid constructed by buildOpticalFlowPyramid.
    .   @param nextImg second input image or pyramid of the same size and the same type as prevImg.
    .   @param prevPts vector of 2D points for which the flow needs to be found; point coordinates must be
    .   single-precision floating-point numbers.
    .   @param nextPts output vector of 2D points (with single-precision floating-point coordinates)
    .   containing the calculated new positions of input features in the second image; when
    .   OPTFLOW_USE_INITIAL_FLOW flag is passed, the vector must have the same size as in the input.
    .   @param status output status vector (of unsigned chars); each element of the vector is set to 1 if
    .   the flow for the corresponding features has been found, otherwise, it is set to 0.
    .   @param err output vector of errors; each element of the vector is set to an error for the
    .   corresponding feature, type of the error measure can be set in flags parameter; if the flow wasn't
    .   found then the error is not defined (use the status parameter to find such cases).
    .   @param winSize size of the search window at each pyramid level.
    .   @param maxLevel 0-based maximal pyramid level number; if set to 0, pyramids are not used (single
    .   level), if set to 1, two levels are used, and so on; if pyramids are passed to input then
    .   algorithm will use as many levels as pyramids have but no more than maxLevel.
    .   @param criteria parameter, specifying the termination criteria of the iterative search algorithm
    .   (after the specified maximum number of iterations criteria.maxCount or when the search window
    .   moves by less than criteria.epsilon.
    .   @param flags operation flags:
    .   -   **OPTFLOW_USE_INITIAL_FLOW** uses initial estimations, stored in nextPts; if the flag is
    .   not set, then prevPts is copied to nextPts and is considered the initial estimate.
    .   -   **OPTFLOW_LK_GET_MIN_EIGENVALS** use minimum eigen values as an error measure (see
    .   minEigThreshold description); if the flag is not set, then L1 distance between patches
    .   around the original and a moved point, divided by number of pixels in a window, is used as a
    .   error measure.
    .   @param minEigThreshold the algorithm calculates the minimum eigen value of a 2x2 normal matrix of
    .   optical flow equations (this matrix is called a spatial gradient matrix in @cite Bouguet00), divided
    .   by number of pixels in a window; if this value is less than minEigThreshold, then a corresponding
    .   feature is filtered out and its flow is not processed, so it allows to remove bad points and get a
    .   performance boost.
    .   
    .   The function implements a sparse iterative version of the Lucas-Kanade optical flow in pyramids. See
    .   @cite Bouguet00 . The function is parallelized with the TBB library.
    .   
    .   @note
    .   
    .   -   An example using the Lucas-Kanade optical flow algorithm can be found at
    .   opencv_source_code/samples/cpp/lkdemo.cpp
    .   -   (Python) An example using the Lucas-Kanade optical flow algorithm can be found at
    .   opencv_source_code/samples/python/lk_track.py
    .   -   (Python) An example using the Lucas-Kanade tracker for homography matching can be found at
    .   opencv_source_code/samples/python/lk_homography.py
    """
    pass

def calibrateCamera(objectPoints, imagePoints, imageSize, cameraMatrix, distCoeffs, rvecs=None, tvecs=None, flags=None, criteria=None): # real signature unknown; restored from __doc__
    """
    calibrateCamera(objectPoints, imagePoints, imageSize, cameraMatrix, distCoeffs[, rvecs[, tvecs[, flags[, criteria]]]]) -> retval, cameraMatrix, distCoeffs, rvecs, tvecs
    .   @overload double calibrateCamera( InputArrayOfArrays objectPoints,
    .   InputArrayOfArrays imagePoints, Size imageSize,
    .   InputOutputArray cameraMatrix, InputOutputArray distCoeffs,
    .   OutputArrayOfArrays rvecs, OutputArrayOfArrays tvecs,
    .   OutputArray stdDeviations, OutputArray perViewErrors,
    .   int flags = 0, TermCriteria criteria = TermCriteria(
    .   TermCriteria::COUNT + TermCriteria::EPS, 30, DBL_EPSILON) )
    """
    pass

def calibrateCameraExtended(objectPoints, imagePoints, imageSize, cameraMatrix, distCoeffs, rvecs=None, tvecs=None, stdDeviationsIntrinsics=None, stdDeviationsExtrinsics=None, perViewErrors=None, flags=None, criteria=None): # real signature unknown; restored from __doc__
    """
    calibrateCameraExtended(objectPoints, imagePoints, imageSize, cameraMatrix, distCoeffs[, rvecs[, tvecs[, stdDeviationsIntrinsics[, stdDeviationsExtrinsics[, perViewErrors[, flags[, criteria]]]]]]]) -> retval, cameraMatrix, distCoeffs, rvecs, tvecs, stdDeviationsIntrinsics, stdDeviationsExtrinsics, perViewErrors
    .   @brief Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern.
    .   
    .   @param objectPoints In the new interface it is a vector of vectors of calibration pattern points in
    .   the calibration pattern coordinate space (e.g. std::vector<std::vector<cv::Vec3f>>). The outer
    .   vector contains as many elements as the number of the pattern views. If the same calibration pattern
    .   is shown in each view and it is fully visible, all the vectors will be the same. Although, it is
    .   possible to use partially occluded patterns, or even different patterns in different views. Then,
    .   the vectors will be different. The points are 3D, but since they are in a pattern coordinate system,
    .   then, if the rig is planar, it may make sense to put the model to a XY coordinate plane so that
    .   Z-coordinate of each input object point is 0.
    .   In the old interface all the vectors of object points from different views are concatenated
    .   together.
    .   @param imagePoints In the new interface it is a vector of vectors of the projections of calibration
    .   pattern points (e.g. std::vector<std::vector<cv::Vec2f>>). imagePoints.size() and
    .   objectPoints.size() and imagePoints[i].size() must be equal to objectPoints[i].size() for each i.
    .   In the old interface all the vectors of object points from different views are concatenated
    .   together.
    .   @param imageSize Size of the image used only to initialize the intrinsic camera matrix.
    .   @param cameraMatrix Output 3x3 floating-point camera matrix
    .   \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ . If CV\_CALIB\_USE\_INTRINSIC\_GUESS
    .   and/or CALIB_FIX_ASPECT_RATIO are specified, some or all of fx, fy, cx, cy must be
    .   initialized before calling the function.
    .   @param distCoeffs Output vector of distortion coefficients
    .   \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ of
    .   4, 5, 8, 12 or 14 elements.
    .   @param rvecs Output vector of rotation vectors (see Rodrigues ) estimated for each pattern view
    .   (e.g. std::vector<cv::Mat>>). That is, each k-th rotation vector together with the corresponding
    .   k-th translation vector (see the next output parameter description) brings the calibration pattern
    .   from the model coordinate space (in which object points are specified) to the world coordinate
    .   space, that is, a real position of the calibration pattern in the k-th pattern view (k=0.. *M* -1).
    .   @param tvecs Output vector of translation vectors estimated for each pattern view.
    .   @param stdDeviationsIntrinsics Output vector of standard deviations estimated for intrinsic parameters.
    .   Order of deviations values:
    .   \f$(f_x, f_y, c_x, c_y, k_1, k_2, p_1, p_2, k_3, k_4, k_5, k_6 , s_1, s_2, s_3,
    .   s_4, \tau_x, \tau_y)\f$ If one of parameters is not estimated, it's deviation is equals to zero.
    .   @param stdDeviationsExtrinsics Output vector of standard deviations estimated for extrinsic parameters.
    .   Order of deviations values: \f$(R_1, T_1, \dotsc , R_M, T_M)\f$ where M is number of pattern views,
    .   \f$R_i, T_i\f$ are concatenated 1x3 vectors.
    .   @param perViewErrors Output vector of the RMS re-projection error estimated for each pattern view.
    .   @param flags Different flags that may be zero or a combination of the following values:
    .   -   **CALIB_USE_INTRINSIC_GUESS** cameraMatrix contains valid initial values of
    .   fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image
    .   center ( imageSize is used), and focal distances are computed in a least-squares fashion.
    .   Note, that if intrinsic parameters are known, there is no need to use this function just to
    .   estimate extrinsic parameters. Use solvePnP instead.
    .   -   **CALIB_FIX_PRINCIPAL_POINT** The principal point is not changed during the global
    .   optimization. It stays at the center or at a different location specified when
    .   CALIB_USE_INTRINSIC_GUESS is set too.
    .   -   **CALIB_FIX_ASPECT_RATIO** The functions considers only fy as a free parameter. The
    .   ratio fx/fy stays the same as in the input cameraMatrix . When
    .   CALIB_USE_INTRINSIC_GUESS is not set, the actual input values of fx and fy are
    .   ignored, only their ratio is computed and used further.
    .   -   **CALIB_ZERO_TANGENT_DIST** Tangential distortion coefficients \f$(p_1, p_2)\f$ are set
    .   to zeros and stay zero.
    .   -   **CALIB_FIX_K1,...,CALIB_FIX_K6** The corresponding radial distortion
    .   coefficient is not changed during the optimization. If CALIB_USE_INTRINSIC_GUESS is
    .   set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
    .   -   **CALIB_RATIONAL_MODEL** Coefficients k4, k5, and k6 are enabled. To provide the
    .   backward compatibility, this extra flag should be explicitly specified to make the
    .   calibration function use the rational model and return 8 coefficients. If the flag is not
    .   set, the function computes and returns only 5 distortion coefficients.
    .   -   **CALIB_THIN_PRISM_MODEL** Coefficients s1, s2, s3 and s4 are enabled. To provide the
    .   backward compatibility, this extra flag should be explicitly specified to make the
    .   calibration function use the thin prism model and return 12 coefficients. If the flag is not
    .   set, the function computes and returns only 5 distortion coefficients.
    .   -   **CALIB_FIX_S1_S2_S3_S4** The thin prism distortion coefficients are not changed during
    .   the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the
    .   supplied distCoeffs matrix is used. Otherwise, it is set to 0.
    .   -   **CALIB_TILTED_MODEL** Coefficients tauX and tauY are enabled. To provide the
    .   backward compatibility, this extra flag should be explicitly specified to make the
    .   calibration function use the tilted sensor model and return 14 coefficients. If the flag is not
    .   set, the function computes and returns only 5 distortion coefficients.
    .   -   **CALIB_FIX_TAUX_TAUY** The coefficients of the tilted sensor model are not changed during
    .   the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the
    .   supplied distCoeffs matrix is used. Otherwise, it is set to 0.
    .   @param criteria Termination criteria for the iterative optimization algorithm.
    .   
    .   @return the overall RMS re-projection error.
    .   
    .   The function estimates the intrinsic camera parameters and extrinsic parameters for each of the
    .   views. The algorithm is based on @cite Zhang2000 and @cite BouguetMCT . The coordinates of 3D object
    .   points and their corresponding 2D projections in each view must be specified. That may be achieved
    .   by using an object with a known geometry and easily detectable feature points. Such an object is
    .   called a calibration rig or calibration pattern, and OpenCV has built-in support for a chessboard as
    .   a calibration rig (see findChessboardCorners ). Currently, initialization of intrinsic parameters
    .   (when CALIB_USE_INTRINSIC_GUESS is not set) is only implemented for planar calibration
    .   patterns (where Z-coordinates of the object points must be all zeros). 3D calibration rigs can also
    .   be used as long as initial cameraMatrix is provided.
    .   
    .   The algorithm performs the following steps:
    .   
    .   -   Compute the initial intrinsic parameters (the option only available for planar calibration
    .   patterns) or read them from the input parameters. The distortion coefficients are all set to
    .   zeros initially unless some of CALIB_FIX_K? are specified.
    .   
    .   -   Estimate the initial camera pose as if the intrinsic parameters have been already known. This is
    .   done using solvePnP .
    .   
    .   -   Run the global Levenberg-Marquardt optimization algorithm to minimize the reprojection error,
    .   that is, the total sum of squared distances between the observed feature points imagePoints and
    .   the projected (using the current estimates for camera parameters and the poses) object points
    .   objectPoints. See projectPoints for details.
    .   
    .   @note
    .   If you use a non-square (=non-NxN) grid and findChessboardCorners for calibration, and
    .   calibrateCamera returns bad values (zero distortion coefficients, an image center very far from
    .   (w/2-0.5,h/2-0.5), and/or large differences between \f$f_x\f$ and \f$f_y\f$ (ratios of 10:1 or more)),
    .   then you have probably used patternSize=cvSize(rows,cols) instead of using
    .   patternSize=cvSize(cols,rows) in findChessboardCorners .
    .   
    .   @sa
    .   findChessboardCorners, solvePnP, initCameraMatrix2D, stereoCalibrate, undistort
    """
    pass

def calibrationMatrixValues(cameraMatrix, imageSize, apertureWidth, apertureHeight): # real signature unknown; restored from __doc__
    """
    calibrationMatrixValues(cameraMatrix, imageSize, apertureWidth, apertureHeight) -> fovx, fovy, focalLength, principalPoint, aspectRatio
    .   @brief Computes useful camera characteristics from the camera matrix.
    .   
    .   @param cameraMatrix Input camera matrix that can be estimated by calibrateCamera or
    .   stereoCalibrate .
    .   @param imageSize Input image size in pixels.
    .   @param apertureWidth Physical width in mm of the sensor.
    .   @param apertureHeight Physical height in mm of the sensor.
    .   @param fovx Output field of view in degrees along the horizontal sensor axis.
    .   @param fovy Output field of view in degrees along the vertical sensor axis.
    .   @param focalLength Focal length of the lens in mm.
    .   @param principalPoint Principal point in mm.
    .   @param aspectRatio \f$f_y/f_x\f$
    .   
    .   The function computes various useful camera characteristics from the previously estimated camera
    .   matrix.
    .   
    .   @note
    .   Do keep in mind that the unity measure 'mm' stands for whatever unit of measure one chooses for
    .   the chessboard pitch (it can thus be any value).
    """
    pass

def CamShift(probImage, window, criteria): # real signature unknown; restored from __doc__
    """
    CamShift(probImage, window, criteria) -> retval, window
    .   @brief Finds an object center, size, and orientation.
    .   
    .   @param probImage Back projection of the object histogram. See calcBackProject.
    .   @param window Initial search window.
    .   @param criteria Stop criteria for the underlying meanShift.
    .   returns
    .   (in old interfaces) Number of iterations CAMSHIFT took to converge
    .   The function implements the CAMSHIFT object tracking algorithm @cite Bradski98 . First, it finds an
    .   object center using meanShift and then adjusts the window size and finds the optimal rotation. The
    .   function returns the rotated rectangle structure that includes the object position, size, and
    .   orientation. The next position of the search window can be obtained with RotatedRect::boundingRect()
    .   
    .   See the OpenCV sample camshiftdemo.c that tracks colored objects.
    .   
    .   @note
    .   -   (Python) A sample explaining the camshift tracking algorithm can be found at
    .   opencv_source_code/samples/python/camshift.py
    """
    pass

def Canny(image, threshold1, threshold2, edges=None, apertureSize=None, L2gradient=None): # real signature unknown; restored from __doc__
    """
    Canny(image, threshold1, threshold2[, edges[, apertureSize[, L2gradient]]]) -> edges
    .   @brief Finds edges in an image using the Canny algorithm @cite Canny86 .
    .   
    .   The function finds edges in the input image image and marks them in the output map edges using the
    .   Canny algorithm. The smallest value between threshold1 and threshold2 is used for edge linking. The
    .   largest value is used to find initial segments of strong edges. See
    .   <http://en.wikipedia.org/wiki/Canny_edge_detector>
    .   
    .   @param image 8-bit input image.
    .   @param edges output edge map; single channels 8-bit image, which has the same size as image .
    .   @param threshold1 first threshold for the hysteresis procedure.
    .   @param threshold2 second threshold for the hysteresis procedure.
    .   @param apertureSize aperture size for the Sobel operator.
    .   @param L2gradient a flag, indicating whether a more accurate \f$L_2\f$ norm
    .   \f$=\sqrt{(dI/dx)^2 + (dI/dy)^2}\f$ should be used to calculate the image gradient magnitude (
    .   L2gradient=true ), or whether the default \f$L_1\f$ norm \f$=|dI/dx|+|dI/dy|\f$ is enough (
    .   L2gradient=false ).
    
    
    
    Canny(dx, dy, threshold1, threshold2[, edges[, L2gradient]]) -> edges
    .   \overload
    .   
    .   Finds edges in an image using the Canny algorithm with custom image gradient.
    .   
    .   @param dx 16-bit x derivative of input image (CV_16SC1 or CV_16SC3).
    .   @param dy 16-bit y derivative of input image (same type as dx).
    .   @param edges,threshold1,threshold2,L2gradient See cv::Canny
    """
    pass

def cartToPolar(x, y, magnitude=None, angle=None, angleInDegrees=None): # real signature unknown; restored from __doc__
    """
    cartToPolar(x, y[, magnitude[, angle[, angleInDegrees]]]) -> magnitude, angle
    .   @brief Calculates the magnitude and angle of 2D vectors.
    .   
    .   The function cv::cartToPolar calculates either the magnitude, angle, or both
    .   for every 2D vector (x(I),y(I)):
    .   \f[\begin{array}{l} \texttt{magnitude} (I)= \sqrt{\texttt{x}(I)^2+\texttt{y}(I)^2} , \\ \texttt{angle} (I)= \texttt{atan2} ( \texttt{y} (I), \texttt{x} (I))[ \cdot180 / \pi ] \end{array}\f]
    .   
    .   The angles are calculated with accuracy about 0.3 degrees. For the point
    .   (0,0), the angle is set to 0.
    .   @param x array of x-coordinates; this must be a single-precision or
    .   double-precision floating-point array.
    .   @param y array of y-coordinates, that must have the same size and same type as x.
    .   @param magnitude output array of magnitudes of the same size and type as x.
    .   @param angle output array of angles that has the same size and type as
    .   x; the angles are measured in radians (from 0 to 2\*Pi) or in degrees (0 to 360 degrees).
    .   @param angleInDegrees a flag, indicating whether the angles are measured
    .   in radians (which is by default), or in degrees.
    .   @sa Sobel, Scharr
    """
    pass

def CascadeClassifier(filename=None): # real signature unknown; restored from __doc__
    """
    CascadeClassifier([filename]) -> <CascadeClassifier object>
    .
    """
    pass

def CascadeClassifier_convert(oldcascade, newcascade): # real signature unknown; restored from __doc__
    """
    CascadeClassifier_convert(oldcascade, newcascade) -> retval
    .
    """
    pass

def checkHardwareSupport(feature): # real signature unknown; restored from __doc__
    """
    checkHardwareSupport(feature) -> retval
    .   @brief Returns true if the specified feature is supported by the host hardware.
    .   
    .   The function returns true if the host hardware supports the specified feature. When user calls
    .   setUseOptimized(false), the subsequent calls to checkHardwareSupport() will return false until
    .   setUseOptimized(true) is called. This way user can dynamically switch on and off the optimized code
    .   in OpenCV.
    .   @param feature The feature of interest, one of cv::CpuFeatures
    """
    pass

def checkRange(a, quiet=None, minVal=None, maxVal=None): # real signature unknown; restored from __doc__
    """
    checkRange(a[, quiet[, minVal[, maxVal]]]) -> retval, pos
    .   @brief Checks every element of an input array for invalid values.
    .   
    .   The function cv::checkRange checks that every array element is neither NaN nor infinite. When minVal \>
    .   -DBL_MAX and maxVal \< DBL_MAX, the function also checks that each value is between minVal and
    .   maxVal. In case of multi-channel arrays, each channel is processed independently. If some values
    .   are out of range, position of the first outlier is stored in pos (when pos != NULL). Then, the
    .   function either returns false (when quiet=true) or throws an exception.
    .   @param a input array.
    .   @param quiet a flag, indicating whether the functions quietly return false when the array elements
    .   are out of range or they throw an exception.
    .   @param pos optional output parameter, when not NULL, must be a pointer to array of src.dims
    .   elements.
    .   @param minVal inclusive lower boundary of valid values range.
    .   @param maxVal exclusive upper boundary of valid values range.
    """
    pass

def circle(img, center, radius, color, thickness=None, lineType=None, shift=None): # real signature unknown; restored from __doc__
    """
    circle(img, center, radius, color[, thickness[, lineType[, shift]]]) -> img
    .   @brief Draws a circle.
    .   
    .   The function circle draws a simple or filled circle with a given center and radius.
    .   @param img Image where the circle is drawn.
    .   @param center Center of the circle.
    .   @param radius Radius of the circle.
    .   @param color Circle color.
    .   @param thickness Thickness of the circle outline, if positive. Negative thickness means that a
    .   filled circle is to be drawn.
    .   @param lineType Type of the circle boundary. See the line description.
    .   @param shift Number of fractional bits in the coordinates of the center and in the radius value.
    """
    pass

def CirclesGridFinderParameters(): # real signature unknown; restored from __doc__
    """
    CirclesGridFinderParameters() -> <CirclesGridFinderParameters object>
    .
    """
    pass

def clipLine(imgRect, pt1, pt2): # real signature unknown; restored from __doc__
    """
    clipLine(imgRect, pt1, pt2) -> retval, pt1, pt2
    .   @overload
    .   @param imgRect Image rectangle.
    .   @param pt1 First line point.
    .   @param pt2 Second line point.
    """
    pass

def colorChange(src, mask, dst=None, red_mul=None, green_mul=None, blue_mul=None): # real signature unknown; restored from __doc__
    """
    colorChange(src, mask[, dst[, red_mul[, green_mul[, blue_mul]]]]) -> dst
    .   @brief Given an original color image, two differently colored versions of this image can be mixed
    .   seamlessly.
    .   
    .   @param src Input 8-bit 3-channel image.
    .   @param mask Input 8-bit 1 or 3-channel image.
    .   @param dst Output image with the same size and type as src .
    .   @param red_mul R-channel multiply factor.
    .   @param green_mul G-channel multiply factor.
    .   @param blue_mul B-channel multiply factor.
    .   
    .   Multiplication factor is between .5 to 2.5.
    """
    pass

def compare(src1, src2, cmpop, dst=None): # real signature unknown; restored from __doc__
    """
    compare(src1, src2, cmpop[, dst]) -> dst
    .   @brief Performs the per-element comparison of two arrays or an array and scalar value.
    .   
    .   The function compares:
    .   *   Elements of two arrays when src1 and src2 have the same size:
    .   \f[\texttt{dst} (I) =  \texttt{src1} (I)  \,\texttt{cmpop}\, \texttt{src2} (I)\f]
    .   *   Elements of src1 with a scalar src2 when src2 is constructed from
    .   Scalar or has a single element:
    .   \f[\texttt{dst} (I) =  \texttt{src1}(I) \,\texttt{cmpop}\,  \texttt{src2}\f]
    .   *   src1 with elements of src2 when src1 is constructed from Scalar or
    .   has a single element:
    .   \f[\texttt{dst} (I) =  \texttt{src1}  \,\texttt{cmpop}\, \texttt{src2} (I)\f]
    .   When the comparison result is true, the corresponding element of output
    .   array is set to 255. The comparison operations can be replaced with the
    .   equivalent matrix expressions:
    .   @code{.cpp}
    .   Mat dst1 = src1 >= src2;
    .   Mat dst2 = src1 < 8;
    .   ...
    .   @endcode
    .   @param src1 first input array or a scalar; when it is an array, it must have a single channel.
    .   @param src2 second input array or a scalar; when it is an array, it must have a single channel.
    .   @param dst output array of type ref CV_8U that has the same size and the same number of channels as
    .   the input arrays.
    .   @param cmpop a flag, that specifies correspondence between the arrays (cv::CmpTypes)
    .   @sa checkRange, min, max, threshold
    """
    pass

def compareHist(H1, H2, method): # real signature unknown; restored from __doc__
    """
    compareHist(H1, H2, method) -> retval
    .   @brief Compares two histograms.
    .   
    .   The function cv::compareHist compares two dense or two sparse histograms using the specified method.
    .   
    .   The function returns \f$d(H_1, H_2)\f$ .
    .   
    .   While the function works well with 1-, 2-, 3-dimensional dense histograms, it may not be suitable
    .   for high-dimensional sparse histograms. In such histograms, because of aliasing and sampling
    .   problems, the coordinates of non-zero histogram bins can slightly shift. To compare such histograms
    .   or more general sparse configurations of weighted points, consider using the cv::EMD function.
    .   
    .   @param H1 First compared histogram.
    .   @param H2 Second compared histogram of the same size as H1 .
    .   @param method Comparison method, see cv::HistCompMethods
    """
    pass

def completeSymm(mtx, lowerToUpper=None): # real signature unknown; restored from __doc__
    """
    completeSymm(mtx[, lowerToUpper]) -> mtx
    .   @brief Copies the lower or the upper half of a square matrix to another half.
    .   
    .   The function cv::completeSymm copies the lower half of a square matrix to
    .   its another half. The matrix diagonal remains unchanged:
    .   *   \f$\texttt{mtx}_{ij}=\texttt{mtx}_{ji}\f$ for \f$i > j\f$ if
    .   lowerToUpper=false
    .   *   \f$\texttt{mtx}_{ij}=\texttt{mtx}_{ji}\f$ for \f$i < j\f$ if
    .   lowerToUpper=true
    .   @param mtx input-output floating-point square matrix.
    .   @param lowerToUpper operation flag; if true, the lower half is copied to
    .   the upper half. Otherwise, the upper half is copied to the lower half.
    .   @sa flip, transpose
    """
    pass

def composeRT(rvec1, tvec1, rvec2, tvec2, rvec3=None, tvec3=None, dr3dr1=None, dr3dt1=None, dr3dr2=None, dr3dt2=None, dt3dr1=None, dt3dt1=None, dt3dr2=None, dt3dt2=None): # real signature unknown; restored from __doc__
    """
    composeRT(rvec1, tvec1, rvec2, tvec2[, rvec3[, tvec3[, dr3dr1[, dr3dt1[, dr3dr2[, dr3dt2[, dt3dr1[, dt3dt1[, dt3dr2[, dt3dt2]]]]]]]]]]) -> rvec3, tvec3, dr3dr1, dr3dt1, dr3dr2, dr3dt2, dt3dr1, dt3dt1, dt3dr2, dt3dt2
    .   @brief Combines two rotation-and-shift transformations.
    .   
    .   @param rvec1 First rotation vector.
    .   @param tvec1 First translation vector.
    .   @param rvec2 Second rotation vector.
    .   @param tvec2 Second translation vector.
    .   @param rvec3 Output rotation vector of the superposition.
    .   @param tvec3 Output translation vector of the superposition.
    .   @param dr3dr1
    .   @param dr3dt1
    .   @param dr3dr2
    .   @param dr3dt2
    .   @param dt3dr1
    .   @param dt3dt1
    .   @param dt3dr2
    .   @param dt3dt2 Optional output derivatives of rvec3 or tvec3 with regard to rvec1, rvec2, tvec1 and
    .   tvec2, respectively.
    .   
    .   The functions compute:
    .   
    .   \f[\begin{array}{l} \texttt{rvec3} =  \mathrm{rodrigues} ^{-1} \left ( \mathrm{rodrigues} ( \texttt{rvec2} )  \cdot \mathrm{rodrigues} ( \texttt{rvec1} ) \right )  \\ \texttt{tvec3} =  \mathrm{rodrigues} ( \texttt{rvec2} )  \cdot \texttt{tvec1} +  \texttt{tvec2} \end{array} ,\f]
    .   
    .   where \f$\mathrm{rodrigues}\f$ denotes a rotation vector to a rotation matrix transformation, and
    .   \f$\mathrm{rodrigues}^{-1}\f$ denotes the inverse transformation. See Rodrigues for details.
    .   
    .   Also, the functions can compute the derivatives of the output vectors with regards to the input
    .   vectors (see matMulDeriv ). The functions are used inside stereoCalibrate but can also be used in
    .   your own code where Levenberg-Marquardt or another gradient-based solver is used to optimize a
    .   function that contains a matrix multiplication.
    """
    pass

def computeCorrespondEpilines(points, whichImage, F, lines=None): # real signature unknown; restored from __doc__
    """
    computeCorrespondEpilines(points, whichImage, F[, lines]) -> lines
    .   @brief For points in an image of a stereo pair, computes the corresponding epilines in the other image.
    .   
    .   @param points Input points. \f$N \times 1\f$ or \f$1 \times N\f$ matrix of type CV_32FC2 or
    .   vector\<Point2f\> .
    .   @param whichImage Index of the image (1 or 2) that contains the points .
    .   @param F Fundamental matrix that can be estimated using findFundamentalMat or stereoRectify .
    .   @param lines Output vector of the epipolar lines corresponding to the points in the other image.
    .   Each line \f$ax + by + c=0\f$ is encoded by 3 numbers \f$(a, b, c)\f$ .
    .   
    .   For every point in one of the two images of a stereo pair, the function finds the equation of the
    .   corresponding epipolar line in the other image.
    .   
    .   From the fundamental matrix definition (see findFundamentalMat ), line \f$l^{(2)}_i\f$ in the second
    .   image for the point \f$p^{(1)}_i\f$ in the first image (when whichImage=1 ) is computed as:
    .   
    .   \f[l^{(2)}_i = F p^{(1)}_i\f]
    .   
    .   And vice versa, when whichImage=2, \f$l^{(1)}_i\f$ is computed from \f$p^{(2)}_i\f$ as:
    .   
    .   \f[l^{(1)}_i = F^T p^{(2)}_i\f]
    .   
    .   Line coefficients are defined up to a scale. They are normalized so that \f$a_i^2+b_i^2=1\f$ .
    """
    pass

def connectedComponents(image, labels=None, connectivity=None, ltype=None): # real signature unknown; restored from __doc__
    """
    connectedComponents(image[, labels[, connectivity[, ltype]]]) -> retval, labels
    .   @overload
    .   
    .   @param image the 8-bit single-channel image to be labeled
    .   @param labels destination labeled image
    .   @param connectivity 8 or 4 for 8-way or 4-way connectivity respectively
    .   @param ltype output image label type. Currently CV_32S and CV_16U are supported.
    """
    pass

def connectedComponentsWithAlgorithm(image, connectivity, ltype, ccltype, labels=None): # real signature unknown; restored from __doc__
    """
    connectedComponentsWithAlgorithm(image, connectivity, ltype, ccltype[, labels]) -> retval, labels
    .   @brief computes the connected components labeled image of boolean image
    .   
    .   image with 4 or 8 way connectivity - returns N, the total number of labels [0, N-1] where 0
    .   represents the background label. ltype specifies the output label image type, an important
    .   consideration based on the total number of labels or alternatively the total number of pixels in
    .   the source image. ccltype specifies the connected components labeling algorithm to use, currently
    .   Grana (BBDT) and Wu's (SAUF) algorithms are supported, see the cv::ConnectedComponentsAlgorithmsTypes
    .   for details. Note that SAUF algorithm forces a row major ordering of labels while BBDT does not.
    .   This function uses parallel version of both Grana and Wu's algorithms if at least one allowed
    .   parallel framework is enabled and if the rows of the image are at least twice the number returned by getNumberOfCPUs.
    .   
    .   @param image the 8-bit single-channel image to be labeled
    .   @param labels destination labeled image
    .   @param connectivity 8 or 4 for 8-way or 4-way connectivity respectively
    .   @param ltype output image label type. Currently CV_32S and CV_16U are supported.
    .   @param ccltype connected components algorithm type (see the cv::ConnectedComponentsAlgorithmsTypes).
    """
    pass

def connectedComponentsWithStats(image, labels=None, stats=None, centroids=None, connectivity=None, ltype=None): # real signature unknown; restored from __doc__
    """
    connectedComponentsWithStats(image[, labels[, stats[, centroids[, connectivity[, ltype]]]]]) -> retval, labels, stats, centroids
    .   @overload
    .   @param image the 8-bit single-channel image to be labeled
    .   @param labels destination labeled image
    .   @param stats statistics output for each label, including the background label, see below for
    .   available statistics. Statistics are accessed via stats(label, COLUMN) where COLUMN is one of
    .   cv::ConnectedComponentsTypes. The data type is CV_32S.
    .   @param centroids centroid output for each label, including the background label. Centroids are
    .   accessed via centroids(label, 0) for x and centroids(label, 1) for y. The data type CV_64F.
    .   @param connectivity 8 or 4 for 8-way or 4-way connectivity respectively
    .   @param ltype output image label type. Currently CV_32S and CV_16U are supported.
    """
    pass

def connectedComponentsWithStatsWithAlgorithm(image, connectivity, ltype, ccltype, labels=None, stats=None, centroids=None): # real signature unknown; restored from __doc__
    """
    connectedComponentsWithStatsWithAlgorithm(image, connectivity, ltype, ccltype[, labels[, stats[, centroids]]]) -> retval, labels, stats, centroids
    .   @brief computes the connected components labeled image of boolean image and also produces a statistics output for each label
    .   
    .   image with 4 or 8 way connectivity - returns N, the total number of labels [0, N-1] where 0
    .   represents the background label. ltype specifies the output label image type, an important
    .   consideration based on the total number of labels or alternatively the total number of pixels in
    .   the source image. ccltype specifies the connected components labeling algorithm to use, currently
    .   Grana's (BBDT) and Wu's (SAUF) algorithms are supported, see the cv::ConnectedComponentsAlgorithmsTypes
    .   for details. Note that SAUF algorithm forces a row major ordering of labels while BBDT does not.
    .   This function uses parallel version of both Grana and Wu's algorithms (statistics included) if at least one allowed
    .   parallel framework is enabled and if the rows of the image are at least twice the number returned by getNumberOfCPUs.
    .   
    .   @param image the 8-bit single-channel image to be labeled
    .   @param labels destination labeled image
    .   @param stats statistics output for each label, including the background label, see below for
    .   available statistics. Statistics are accessed via stats(label, COLUMN) where COLUMN is one of
    .   cv::ConnectedComponentsTypes. The data type is CV_32S.
    .   @param centroids centroid output for each label, including the background label. Centroids are
    .   accessed via centroids(label, 0) for x and centroids(label, 1) for y. The data type CV_64F.
    .   @param connectivity 8 or 4 for 8-way or 4-way connectivity respectively
    .   @param ltype output image label type. Currently CV_32S and CV_16U are supported.
    .   @param ccltype connected components algorithm type (see the cv::ConnectedComponentsAlgorithmsTypes).
    """
    pass

def contourArea(contour, oriented=None): # real signature unknown; restored from __doc__
    """
    contourArea(contour[, oriented]) -> retval
    .   @brief Calculates a contour area.
    .   
    .   The function computes a contour area. Similarly to moments , the area is computed using the Green
    .   formula. Thus, the returned area and the number of non-zero pixels, if you draw the contour using
    .   drawContours or fillPoly , can be different. Also, the function will most certainly give a wrong
    .   results for contours with self-intersections.
    .   
    .   Example:
    .   @code
    .   vector<Point> contour;
    .   contour.push_back(Point2f(0, 0));
    .   contour.push_back(Point2f(10, 0));
    .   contour.push_back(Point2f(10, 10));
    .   contour.push_back(Point2f(5, 4));
    .   
    .   double area0 = contourArea(contour);
    .   vector<Point> approx;
    .   approxPolyDP(contour, approx, 5, true);
    .   double area1 = contourArea(approx);
    .   
    .   cout << "area0 =" << area0 << endl <<
    .   "area1 =" << area1 << endl <<
    .   "approx poly vertices" << approx.size() << endl;
    .   @endcode
    .   @param contour Input vector of 2D points (contour vertices), stored in std::vector or Mat.
    .   @param oriented Oriented area flag. If it is true, the function returns a signed area value,
    .   depending on the contour orientation (clockwise or counter-clockwise). Using this feature you can
    .   determine orientation of a contour by taking the sign of an area. By default, the parameter is
    .   false, which means that the absolute value is returned.
    """
    pass

def convertFp16(src, dst=None): # real signature unknown; restored from __doc__
    """
    convertFp16(src[, dst]) -> dst
    .   @brief Converts an array to half precision floating number.
    .   
    .   This function converts FP32 (single precision floating point) from/to FP16 (half precision floating point).  The input array has to have type of CV_32F or
    .   CV_16S to represent the bit depth.  If the input array is neither of them, the function will raise an error.
    .   The format of half precision floating point is defined in IEEE 754-2008.
    .   
    .   @param src input array.
    .   @param dst output array.
    """
    pass

def convertMaps(map1, map2, dstmap1type, dstmap1=None, dstmap2=None, nninterpolation=None): # real signature unknown; restored from __doc__
    """
    convertMaps(map1, map2, dstmap1type[, dstmap1[, dstmap2[, nninterpolation]]]) -> dstmap1, dstmap2
    .   @brief Converts image transformation maps from one representation to another.
    .   
    .   The function converts a pair of maps for remap from one representation to another. The following
    .   options ( (map1.type(), map2.type()) \f$\rightarrow\f$ (dstmap1.type(), dstmap2.type()) ) are
    .   supported:
    .   
    .   - \f$\texttt{(CV_32FC1, CV_32FC1)} \rightarrow \texttt{(CV_16SC2, CV_16UC1)}\f$. This is the
    .   most frequently used conversion operation, in which the original floating-point maps (see remap )
    .   are converted to a more compact and much faster fixed-point representation. The first output array
    .   contains the rounded coordinates and the second array (created only when nninterpolation=false )
    .   contains indices in the interpolation tables.
    .   
    .   - \f$\texttt{(CV_32FC2)} \rightarrow \texttt{(CV_16SC2, CV_16UC1)}\f$. The same as above but
    .   the original maps are stored in one 2-channel matrix.
    .   
    .   - Reverse conversion. Obviously, the reconstructed floating-point maps will not be exactly the same
    .   as the originals.
    .   
    .   @param map1 The first input map of type CV_16SC2, CV_32FC1, or CV_32FC2 .
    .   @param map2 The second input map of type CV_16UC1, CV_32FC1, or none (empty matrix),
    .   respectively.
    .   @param dstmap1 The first output map that has the type dstmap1type and the same size as src .
    .   @param dstmap2 The second output map.
    .   @param dstmap1type Type of the first output map that should be CV_16SC2, CV_32FC1, or
    .   CV_32FC2 .
    .   @param nninterpolation Flag indicating whether the fixed-point maps are used for the
    .   nearest-neighbor or for a more complex interpolation.
    .   
    .   @sa  remap, undistort, initUndistortRectifyMap
    """
    pass

def convertPointsFromHomogeneous(src, dst=None): # real signature unknown; restored from __doc__
    """
    convertPointsFromHomogeneous(src[, dst]) -> dst
    .   @brief Converts points from homogeneous to Euclidean space.
    .   
    .   @param src Input vector of N-dimensional points.
    .   @param dst Output vector of N-1-dimensional points.
    .   
    .   The function converts points homogeneous to Euclidean space using perspective projection. That is,
    .   each point (x1, x2, ... x(n-1), xn) is converted to (x1/xn, x2/xn, ..., x(n-1)/xn). When xn=0, the
    .   output point coordinates will be (0,0,0,...).
    """
    pass

def convertPointsToHomogeneous(src, dst=None): # real signature unknown; restored from __doc__
    """
    convertPointsToHomogeneous(src[, dst]) -> dst
    .   @brief Converts points from Euclidean to homogeneous space.
    .   
    .   @param src Input vector of N-dimensional points.
    .   @param dst Output vector of N+1-dimensional points.
    .   
    .   The function converts points from Euclidean to homogeneous space by appending 1's to the tuple of
    .   point coordinates. That is, each point (x1, x2, ..., xn) is converted to (x1, x2, ..., xn, 1).
    """
    pass

def convertScaleAbs(src, dst=None, alpha=None, beta=None): # real signature unknown; restored from __doc__
    """
    convertScaleAbs(src[, dst[, alpha[, beta]]]) -> dst
    .   @brief Scales, calculates absolute values, and converts the result to 8-bit.
    .   
    .   On each element of the input array, the function convertScaleAbs
    .   performs three operations sequentially: scaling, taking an absolute
    .   value, conversion to an unsigned 8-bit type:
    .   \f[\texttt{dst} (I)= \texttt{saturate\_cast<uchar>} (| \texttt{src} (I)* \texttt{alpha} +  \texttt{beta} |)\f]
    .   In case of multi-channel arrays, the function processes each channel
    .   independently. When the output is not 8-bit, the operation can be
    .   emulated by calling the Mat::convertTo method (or by using matrix
    .   expressions) and then by calculating an absolute value of the result.
    .   For example:
    .   @code{.cpp}
    .   Mat_<float> A(30,30);
    .   randu(A, Scalar(-100), Scalar(100));
    .   Mat_<float> B = A*5 + 3;
    .   B = abs(B);
    .   // Mat_<float> B = abs(A*5+3) will also do the job,
    .   // but it will allocate a temporary matrix
    .   @endcode
    .   @param src input array.
    .   @param dst output array.
    .   @param alpha optional scale factor.
    .   @param beta optional delta added to the scaled values.
    .   @sa  Mat::convertTo, cv::abs(const Mat&)
    """
    pass

def convexHull(points, hull=None, clockwise=None, returnPoints=None): # real signature unknown; restored from __doc__
    """
    convexHull(points[, hull[, clockwise[, returnPoints]]]) -> hull
    .   @brief Finds the convex hull of a point set.
    .   
    .   The function cv::convexHull finds the convex hull of a 2D point set using the Sklansky's algorithm @cite Sklansky82
    .   that has *O(N logN)* complexity in the current implementation. See the OpenCV sample convexhull.cpp
    .   that demonstrates the usage of different function variants.
    .   
    .   @param points Input 2D point set, stored in std::vector or Mat.
    .   @param hull Output convex hull. It is either an integer vector of indices or vector of points. In
    .   the first case, the hull elements are 0-based indices of the convex hull points in the original
    .   array (since the set of convex hull points is a subset of the original point set). In the second
    .   case, hull elements are the convex hull points themselves.
    .   @param clockwise Orientation flag. If it is true, the output convex hull is oriented clockwise.
    .   Otherwise, it is oriented counter-clockwise. The assumed coordinate system has its X axis pointing
    .   to the right, and its Y axis pointing upwards.
    .   @param returnPoints Operation flag. In case of a matrix, when the flag is true, the function
    .   returns convex hull points. Otherwise, it returns indices of the convex hull points. When the
    .   output array is std::vector, the flag is ignored, and the output depends on the type of the
    .   vector: std::vector\<int\> implies returnPoints=false, std::vector\<Point\> implies
    .   returnPoints=true.
    """
    pass

def convexityDefects(contour, convexhull, convexityDefects=None): # real signature unknown; restored from __doc__
    """
    convexityDefects(contour, convexhull[, convexityDefects]) -> convexityDefects
    .   @brief Finds the convexity defects of a contour.
    .   
    .   The figure below displays convexity defects of a hand contour:
    .   
    .   ![image](pics/defects.png)
    .   
    .   @param contour Input contour.
    .   @param convexhull Convex hull obtained using convexHull that should contain indices of the contour
    .   points that make the hull.
    .   @param convexityDefects The output vector of convexity defects. In C++ and the new Python/Java
    .   interface each convexity defect is represented as 4-element integer vector (a.k.a. cv::Vec4i):
    .   (start_index, end_index, farthest_pt_index, fixpt_depth), where indices are 0-based indices
    .   in the original contour of the convexity defect beginning, end and the farthest point, and
    .   fixpt_depth is fixed-point approximation (with 8 fractional bits) of the distance between the
    .   farthest contour point and the hull. That is, to get the floating-point value of the depth will be
    .   fixpt_depth/256.0.
    """
    pass

def copyMakeBorder(src, top, bottom, left, right, borderType, dst=None, value=None): # real signature unknown; restored from __doc__
    """
    copyMakeBorder(src, top, bottom, left, right, borderType[, dst[, value]]) -> dst
    .   @brief Forms a border around an image.
    .   
    .   The function copies the source image into the middle of the destination image. The areas to the
    .   left, to the right, above and below the copied source image will be filled with extrapolated
    .   pixels. This is not what filtering functions based on it do (they extrapolate pixels on-fly), but
    .   what other more complex functions, including your own, may do to simplify image boundary handling.
    .   
    .   The function supports the mode when src is already in the middle of dst . In this case, the
    .   function does not copy src itself but simply constructs the border, for example:
    .   
    .   @code{.cpp}
    .   // let border be the same in all directions
    .   int border=2;
    .   // constructs a larger image to fit both the image and the border
    .   Mat gray_buf(rgb.rows + border*2, rgb.cols + border*2, rgb.depth());
    .   // select the middle part of it w/o copying data
    .   Mat gray(gray_canvas, Rect(border, border, rgb.cols, rgb.rows));
    .   // convert image from RGB to grayscale
    .   cvtColor(rgb, gray, COLOR_RGB2GRAY);
    .   // form a border in-place
    .   copyMakeBorder(gray, gray_buf, border, border,
    .   border, border, BORDER_REPLICATE);
    .   // now do some custom filtering ...
    .   ...
    .   @endcode
    .   @note When the source image is a part (ROI) of a bigger image, the function will try to use the
    .   pixels outside of the ROI to form a border. To disable this feature and always do extrapolation, as
    .   if src was not a ROI, use borderType | BORDER_ISOLATED.
    .   
    .   @param src Source image.
    .   @param dst Destination image of the same type as src and the size Size(src.cols+left+right,
    .   src.rows+top+bottom) .
    .   @param top
    .   @param bottom
    .   @param left
    .   @param right Parameter specifying how many pixels in each direction from the source image rectangle
    .   to extrapolate. For example, top=1, bottom=1, left=1, right=1 mean that 1 pixel-wide border needs
    .   to be built.
    .   @param borderType Border type. See borderInterpolate for details.
    .   @param value Border value if borderType==BORDER_CONSTANT .
    .   
    .   @sa  borderInterpolate
    """
    pass

def cornerEigenValsAndVecs(src, blockSize, ksize, dst=None, borderType=None): # real signature unknown; restored from __doc__
    """
    cornerEigenValsAndVecs(src, blockSize, ksize[, dst[, borderType]]) -> dst
    .   @brief Calculates eigenvalues and eigenvectors of image blocks for corner detection.
    .   
    .   For every pixel \f$p\f$ , the function cornerEigenValsAndVecs considers a blockSize \f$\times\f$ blockSize
    .   neighborhood \f$S(p)\f$ . It calculates the covariation matrix of derivatives over the neighborhood as:
    .   
    .   \f[M =  \begin{bmatrix} \sum _{S(p)}(dI/dx)^2 &  \sum _{S(p)}dI/dx dI/dy  \\ \sum _{S(p)}dI/dx dI/dy &  \sum _{S(p)}(dI/dy)^2 \end{bmatrix}\f]
    .   
    .   where the derivatives are computed using the Sobel operator.
    .   
    .   After that, it finds eigenvectors and eigenvalues of \f$M\f$ and stores them in the destination image as
    .   \f$(\lambda_1, \lambda_2, x_1, y_1, x_2, y_2)\f$ where
    .   
    .   -   \f$\lambda_1, \lambda_2\f$ are the non-sorted eigenvalues of \f$M\f$
    .   -   \f$x_1, y_1\f$ are the eigenvectors corresponding to \f$\lambda_1\f$
    .   -   \f$x_2, y_2\f$ are the eigenvectors corresponding to \f$\lambda_2\f$
    .   
    .   The output of the function can be used for robust edge or corner detection.
    .   
    .   @param src Input single-channel 8-bit or floating-point image.
    .   @param dst Image to store the results. It has the same size as src and the type CV_32FC(6) .
    .   @param blockSize Neighborhood size (see details below).
    .   @param ksize Aperture parameter for the Sobel operator.
    .   @param borderType Pixel extrapolation method. See cv::BorderTypes.
    .   
    .   @sa  cornerMinEigenVal, cornerHarris, preCornerDetect
    """
    pass

def cornerHarris(src, blockSize, ksize, k, dst=None, borderType=None): # real signature unknown; restored from __doc__
    """
    cornerHarris(src, blockSize, ksize, k[, dst[, borderType]]) -> dst
    .   @brief Harris corner detector.
    .   
    .   The function runs the Harris corner detector on the image. Similarly to cornerMinEigenVal and
    .   cornerEigenValsAndVecs , for each pixel \f$(x, y)\f$ it calculates a \f$2\times2\f$ gradient covariance
    .   matrix \f$M^{(x,y)}\f$ over a \f$\texttt{blockSize} \times \texttt{blockSize}\f$ neighborhood. Then, it
    .   computes the following characteristic:
    .   
    .   \f[\texttt{dst} (x,y) =  \mathrm{det} M^{(x,y)} - k  \cdot \left ( \mathrm{tr} M^{(x,y)} \right )^2\f]
    .   
    .   Corners in the image can be found as the local maxima of this response map.
    .   
    .   @param src Input single-channel 8-bit or floating-point image.
    .   @param dst Image to store the Harris detector responses. It has the type CV_32FC1 and the same
    .   size as src .
    .   @param blockSize Neighborhood size (see the details on cornerEigenValsAndVecs ).
    .   @param ksize Aperture parameter for the Sobel operator.
    .   @param k Harris detector free parameter. See the formula below.
    .   @param borderType Pixel extrapolation method. See cv::BorderTypes.
    """
    pass

def cornerMinEigenVal(src, blockSize, dst=None, ksize=None, borderType=None): # real signature unknown; restored from __doc__
    """
    cornerMinEigenVal(src, blockSize[, dst[, ksize[, borderType]]]) -> dst
    .   @brief Calculates the minimal eigenvalue of gradient matrices for corner detection.
    .   
    .   The function is similar to cornerEigenValsAndVecs but it calculates and stores only the minimal
    .   eigenvalue of the covariance matrix of derivatives, that is, \f$\min(\lambda_1, \lambda_2)\f$ in terms
    .   of the formulae in the cornerEigenValsAndVecs description.
    .   
    .   @param src Input single-channel 8-bit or floating-point image.
    .   @param dst Image to store the minimal eigenvalues. It has the type CV_32FC1 and the same size as
    .   src .
    .   @param blockSize Neighborhood size (see the details on cornerEigenValsAndVecs ).
    .   @param ksize Aperture parameter for the Sobel operator.
    .   @param borderType Pixel extrapolation method. See cv::BorderTypes.
    """
    pass

def cornerSubPix(image, corners, winSize, zeroZone, criteria): # real signature unknown; restored from __doc__
    """
    cornerSubPix(image, corners, winSize, zeroZone, criteria) -> corners
    .   @brief Refines the corner locations.
    .   
    .   The function iterates to find the sub-pixel accurate location of corners or radial saddle points, as
    .   shown on the figure below.
    .   
    .   ![image](pics/cornersubpix.png)
    .   
    .   Sub-pixel accurate corner locator is based on the observation that every vector from the center \f$q\f$
    .   to a point \f$p\f$ located within a neighborhood of \f$q\f$ is orthogonal to the image gradient at \f$p\f$
    .   subject to image and measurement noise. Consider the expression:
    .   
    .   \f[\epsilon _i = {DI_{p_i}}^T  \cdot (q - p_i)\f]
    .   
    .   where \f${DI_{p_i}}\f$ is an image gradient at one of the points \f$p_i\f$ in a neighborhood of \f$q\f$ . The
    .   value of \f$q\f$ is to be found so that \f$\epsilon_i\f$ is minimized. A system of equations may be set up
    .   with \f$\epsilon_i\f$ set to zero:
    .   
    .   \f[\sum _i(DI_{p_i}  \cdot {DI_{p_i}}^T) -  \sum _i(DI_{p_i}  \cdot {DI_{p_i}}^T  \cdot p_i)\f]
    .   
    .   where the gradients are summed within a neighborhood ("search window") of \f$q\f$ . Calling the first
    .   gradient term \f$G\f$ and the second gradient term \f$b\f$ gives:
    .   
    .   \f[q = G^{-1}  \cdot b\f]
    .   
    .   The algorithm sets the center of the neighborhood window at this new center \f$q\f$ and then iterates
    .   until the center stays within a set threshold.
    .   
    .   @param image Input image.
    .   @param corners Initial coordinates of the input corners and refined coordinates provided for
    .   output.
    .   @param winSize Half of the side length of the search window. For example, if winSize=Size(5,5) ,
    .   then a \f$5*2+1 \times 5*2+1 = 11 \times 11\f$ search window is used.
    .   @param zeroZone Half of the size of the dead region in the middle of the search zone over which
    .   the summation in the formula below is not done. It is used sometimes to avoid possible
    .   singularities of the autocorrelation matrix. The value of (-1,-1) indicates that there is no such
    .   a size.
    .   @param criteria Criteria for termination of the iterative process of corner refinement. That is,
    .   the process of corner position refinement stops either after criteria.maxCount iterations or when
    .   the corner position moves by less than criteria.epsilon on some iteration.
    """
    pass

def correctMatches(F, points1, points2, newPoints1=None, newPoints2=None): # real signature unknown; restored from __doc__
    """
    correctMatches(F, points1, points2[, newPoints1[, newPoints2]]) -> newPoints1, newPoints2
    .   @brief Refines coordinates of corresponding points.
    .   
    .   @param F 3x3 fundamental matrix.
    .   @param points1 1xN array containing the first set of points.
    .   @param points2 1xN array containing the second set of points.
    .   @param newPoints1 The optimized points1.
    .   @param newPoints2 The optimized points2.
    .   
    .   The function implements the Optimal Triangulation Method (see Multiple View Geometry for details).
    .   For each given point correspondence points1[i] \<-\> points2[i], and a fundamental matrix F, it
    .   computes the corrected correspondences newPoints1[i] \<-\> newPoints2[i] that minimize the geometric
    .   error \f$d(points1[i], newPoints1[i])^2 + d(points2[i],newPoints2[i])^2\f$ (where \f$d(a,b)\f$ is the
    .   geometric distance between points \f$a\f$ and \f$b\f$ ) subject to the epipolar constraint
    .   \f$newPoints2^T * F * newPoints1 = 0\f$ .
    """
    pass

def countNonZero(src): # real signature unknown; restored from __doc__
    """
    countNonZero(src) -> retval
    .   @brief Counts non-zero array elements.
    .   
    .   The function returns the number of non-zero elements in src :
    .   \f[\sum _{I: \; \texttt{src} (I) \ne0 } 1\f]
    .   @param src single-channel array.
    .   @sa  mean, meanStdDev, norm, minMaxLoc, calcCovarMatrix
    """
    pass

def createAffineTransformer(fullAffine): # real signature unknown; restored from __doc__
    """
    createAffineTransformer(fullAffine) -> retval
    .   Complete constructor
    """
    pass

def createAlignMTB(max_bits=None, exclude_range=None, cut=None): # real signature unknown; restored from __doc__
    """
    createAlignMTB([, max_bits[, exclude_range[, cut]]]) -> retval
    .   @brief Creates AlignMTB object
    .   
    .   @param max_bits logarithm to the base 2 of maximal shift in each dimension. Values of 5 and 6 are
    .   usually good enough (31 and 63 pixels shift respectively).
    .   @param exclude_range range for exclusion bitmap that is constructed to suppress noise around the
    .   median value.
    .   @param cut if true cuts images, otherwise fills the new regions with zeros.
    """
    pass

def createBackgroundSubtractorKNN(history=None, dist2Threshold=None, detectShadows=None): # real signature unknown; restored from __doc__
    """
    createBackgroundSubtractorKNN([, history[, dist2Threshold[, detectShadows]]]) -> retval
    .   @brief Creates KNN Background Subtractor
    .   
    .   @param history Length of the history.
    .   @param dist2Threshold Threshold on the squared distance between the pixel and the sample to decide
    .   whether a pixel is close to that sample. This parameter does not affect the background update.
    .   @param detectShadows If true, the algorithm will detect shadows and mark them. It decreases the
    .   speed a bit, so if you do not need this feature, set the parameter to false.
    """
    pass

def createBackgroundSubtractorMOG2(history=None, varThreshold=None, detectShadows=None): # real signature unknown; restored from __doc__
    """
    createBackgroundSubtractorMOG2([, history[, varThreshold[, detectShadows]]]) -> retval
    .   @brief Creates MOG2 Background Subtractor
    .   
    .   @param history Length of the history.
    .   @param varThreshold Threshold on the squared Mahalanobis distance between the pixel and the model
    .   to decide whether a pixel is well described by the background model. This parameter does not
    .   affect the background update.
    .   @param detectShadows If true, the algorithm will detect shadows and mark them. It decreases the
    .   speed a bit, so if you do not need this feature, set the parameter to false.
    """
    pass

def createButton(buttonName, onChange, userData=None, buttonType=None, initialButtonState=None): # real signature unknown; restored from __doc__
    """ createButton(buttonName, onChange [, userData, buttonType, initialButtonState]) -> None """
    pass

def createCalibrateDebevec(samples=None, lambda=None, random=None): # real signature unknown; restored from __doc__
    """
    createCalibrateDebevec([, samples[, lambda[, random]]]) -> retval
    .   @brief Creates CalibrateDebevec object
    .   
    .   @param samples number of pixel locations to use
    .   @param lambda smoothness term weight. Greater values produce smoother results, but can alter the
    .   response.
    .   @param random if true sample pixel locations are chosen at random, otherwise they form a
    .   rectangular grid.
    """
    pass

def createCalibrateRobertson(max_iter=None, threshold=None): # real signature unknown; restored from __doc__
    """
    createCalibrateRobertson([, max_iter[, threshold]]) -> retval
    .   @brief Creates CalibrateRobertson object
    .   
    .   @param max_iter maximal number of Gauss-Seidel solver iterations.
    .   @param threshold target difference between results of two successive steps of the minimization.
    """
    pass

def createChiHistogramCostExtractor(nDummies=None, defaultCost=None): # real signature unknown; restored from __doc__
    """
    createChiHistogramCostExtractor([, nDummies[, defaultCost]]) -> retval
    .
    """
    pass

def createCLAHE(clipLimit=None, tileGridSize=None): # real signature unknown; restored from __doc__
    """
    createCLAHE([, clipLimit[, tileGridSize]]) -> retval
    .
    """
    pass

def createEMDHistogramCostExtractor(flag=None, nDummies=None, defaultCost=None): # real signature unknown; restored from __doc__
    """
    createEMDHistogramCostExtractor([, flag[, nDummies[, defaultCost]]]) -> retval
    .
    """
    pass

def createEMDL1HistogramCostExtractor(nDummies=None, defaultCost=None): # real signature unknown; restored from __doc__
    """
    createEMDL1HistogramCostExtractor([, nDummies[, defaultCost]]) -> retval
    .
    """
    pass

def createHanningWindow(winSize, type, dst=None): # real signature unknown; restored from __doc__
    """
    createHanningWindow(winSize, type[, dst]) -> dst
    .   @brief This function computes a Hanning window coefficients in two dimensions.
    .   
    .   See (http://en.wikipedia.org/wiki/Hann_function) and (http://en.wikipedia.org/wiki/Window_function)
    .   for more information.
    .   
    .   An example is shown below:
    .   @code
    .   // create hanning window of size 100x100 and type CV_32F
    .   Mat hann;
    .   createHanningWindow(hann, Size(100, 100), CV_32F);
    .   @endcode
    .   @param dst Destination array to place Hann coefficients in
    .   @param winSize The window size specifications
    .   @param type Created array type
    """
    pass

def createHausdorffDistanceExtractor(distanceFlag=None, rankProp=None): # real signature unknown; restored from __doc__
    """
    createHausdorffDistanceExtractor([, distanceFlag[, rankProp]]) -> retval
    .
    """
    pass

def createLineSegmentDetector(_refine=None, _scale=None, _sigma_scale=None, _quant=None, _ang_th=None, _log_eps=None, _density_th=None, _n_bins=None): # real signature unknown; restored from __doc__
    """
    createLineSegmentDetector([, _refine[, _scale[, _sigma_scale[, _quant[, _ang_th[, _log_eps[, _density_th[, _n_bins]]]]]]]]) -> retval
    .   @brief Creates a smart pointer to a LineSegmentDetector object and initializes it.
    .   
    .   The LineSegmentDetector algorithm is defined using the standard values. Only advanced users may want
    .   to edit those, as to tailor it for their own application.
    .   
    .   @param _refine The way found lines will be refined, see cv::LineSegmentDetectorModes
    .   @param _scale The scale of the image that will be used to find the lines. Range (0..1].
    .   @param _sigma_scale Sigma for Gaussian filter. It is computed as sigma = _sigma_scale/_scale.
    .   @param _quant Bound to the quantization error on the gradient norm.
    .   @param _ang_th Gradient angle tolerance in degrees.
    .   @param _log_eps Detection threshold: -log10(NFA) \> log_eps. Used only when advancent refinement
    .   is chosen.
    .   @param _density_th Minimal density of aligned region points in the enclosing rectangle.
    .   @param _n_bins Number of bins in pseudo-ordering of gradient modulus.
    """
    pass

def createMergeDebevec(): # real signature unknown; restored from __doc__
    """
    createMergeDebevec() -> retval
    .   @brief Creates MergeDebevec object
    """
    pass

def createMergeMertens(contrast_weight=None, saturation_weight=None, exposure_weight=None): # real signature unknown; restored from __doc__
    """
    createMergeMertens([, contrast_weight[, saturation_weight[, exposure_weight]]]) -> retval
    .   @brief Creates MergeMertens object
    .   
    .   @param contrast_weight contrast measure weight. See MergeMertens.
    .   @param saturation_weight saturation measure weight
    .   @param exposure_weight well-exposedness measure weight
    """
    pass

def createMergeRobertson(): # real signature unknown; restored from __doc__
    """
    createMergeRobertson() -> retval
    .   @brief Creates MergeRobertson object
    """
    pass

def createNormHistogramCostExtractor(flag=None, nDummies=None, defaultCost=None): # real signature unknown; restored from __doc__
    """
    createNormHistogramCostExtractor([, flag[, nDummies[, defaultCost]]]) -> retval
    .
    """
    pass

def createOptFlow_DualTVL1(): # real signature unknown; restored from __doc__
    """
    createOptFlow_DualTVL1() -> retval
    .   @brief Creates instance of cv::DenseOpticalFlow
    """
    pass

def createShapeContextDistanceExtractor(nAngularBins=None, nRadialBins=None, innerRadius=None, outerRadius=None, iterations=None, comparer=None, transformer=None): # real signature unknown; restored from __doc__
    """
    createShapeContextDistanceExtractor([, nAngularBins[, nRadialBins[, innerRadius[, outerRadius[, iterations[, comparer[, transformer]]]]]]]) -> retval
    .
    """
    pass

def createStitcher(try_use_gpu=None): # real signature unknown; restored from __doc__
    """
    createStitcher([, try_use_gpu]) -> retval
    .
    """
    pass

def createThinPlateSplineShapeTransformer(regularizationParameter=None): # real signature unknown; restored from __doc__
    """
    createThinPlateSplineShapeTransformer([, regularizationParameter]) -> retval
    .   Complete constructor
    """
    pass

def createTonemap(gamma=None): # real signature unknown; restored from __doc__
    """
    createTonemap([, gamma]) -> retval
    .   @brief Creates simple linear mapper with gamma correction
    .   
    .   @param gamma positive value for gamma correction. Gamma value of 1.0 implies no correction, gamma
    .   equal to 2.2f is suitable for most displays.
    .   Generally gamma \> 1 brightens the image and gamma \< 1 darkens it.
    """
    pass

def createTonemapDrago(gamma=None, saturation=None, bias=None): # real signature unknown; restored from __doc__
    """
    createTonemapDrago([, gamma[, saturation[, bias]]]) -> retval
    .   @brief Creates TonemapDrago object
    .   
    .   @param gamma gamma value for gamma correction. See createTonemap
    .   @param saturation positive saturation enhancement value. 1.0 preserves saturation, values greater
    .   than 1 increase saturation and values less than 1 decrease it.
    .   @param bias value for bias function in [0, 1] range. Values from 0.7 to 0.9 usually give best
    .   results, default value is 0.85.
    """
    pass

def createTonemapDurand(gamma=None, contrast=None, saturation=None, sigma_space=None, sigma_color=None): # real signature unknown; restored from __doc__
    """
    createTonemapDurand([, gamma[, contrast[, saturation[, sigma_space[, sigma_color]]]]]) -> retval
    .   @brief Creates TonemapDurand object
    .   
    .   @param gamma gamma value for gamma correction. See createTonemap
    .   @param contrast resulting contrast on logarithmic scale, i. e. log(max / min), where max and min
    .   are maximum and minimum luminance values of the resulting image.
    .   @param saturation saturation enhancement value. See createTonemapDrago
    .   @param sigma_space bilateral filter sigma in color space
    .   @param sigma_color bilateral filter sigma in coordinate space
    """
    pass

def createTonemapMantiuk(gamma=None, scale=None, saturation=None): # real signature unknown; restored from __doc__
    """
    createTonemapMantiuk([, gamma[, scale[, saturation]]]) -> retval
    .   @brief Creates TonemapMantiuk object
    .   
    .   @param gamma gamma value for gamma correction. See createTonemap
    .   @param scale contrast scale factor. HVS response is multiplied by this parameter, thus compressing
    .   dynamic range. Values from 0.6 to 0.9 produce best results.
    .   @param saturation saturation enhancement value. See createTonemapDrago
    """
    pass

def createTonemapReinhard(gamma=None, intensity=None, light_adapt=None, color_adapt=None): # real signature unknown; restored from __doc__
    """
    createTonemapReinhard([, gamma[, intensity[, light_adapt[, color_adapt]]]]) -> retval
    .   @brief Creates TonemapReinhard object
    .   
    .   @param gamma gamma value for gamma correction. See createTonemap
    .   @param intensity result intensity in [-8, 8] range. Greater intensity produces brighter results.
    .   @param light_adapt light adaptation in [0, 1] range. If 1 adaptation is based only on pixel
    .   value, if 0 it's global, otherwise it's a weighted mean of this two cases.
    .   @param color_adapt chromatic adaptation in [0, 1] range. If 1 channels are treated independently,
    .   if 0 adaptation level is the same for each channel.
    """
    pass

def createTrackbar(trackbarName, windowName, value, count, onChange): # real signature unknown; restored from __doc__
    """ createTrackbar(trackbarName, windowName, value, count, onChange) -> None """
    pass

def cubeRoot(val): # real signature unknown; restored from __doc__
    """
    cubeRoot(val) -> retval
    .   @brief Computes the cube root of an argument.
    .   
    .   The function cubeRoot computes \f$\sqrt[3]{\texttt{val}}\f$. Negative arguments are handled correctly.
    .   NaN and Inf are not handled. The accuracy approaches the maximum possible accuracy for
    .   single-precision data.
    .   @param val A function argument.
    """
    pass

def cvtColor(src, code, dst=None, dstCn=None): # real signature unknown; restored from __doc__
    """
    cvtColor(src, code[, dst[, dstCn]]) -> dst
    .   @brief Converts an image from one color space to another.
    .   
    .   The function converts an input image from one color space to another. In case of a transformation
    .   to-from RGB color space, the order of the channels should be specified explicitly (RGB or BGR). Note
    .   that the default color format in OpenCV is often referred to as RGB but it is actually BGR (the
    .   bytes are reversed). So the first byte in a standard (24-bit) color image will be an 8-bit Blue
    .   component, the second byte will be Green, and the third byte will be Red. The fourth, fifth, and
    .   sixth bytes would then be the second pixel (Blue, then Green, then Red), and so on.
    .   
    .   The conventional ranges for R, G, and B channel values are:
    .   -   0 to 255 for CV_8U images
    .   -   0 to 65535 for CV_16U images
    .   -   0 to 1 for CV_32F images
    .   
    .   In case of linear transformations, the range does not matter. But in case of a non-linear
    .   transformation, an input RGB image should be normalized to the proper value range to get the correct
    .   results, for example, for RGB \f$\rightarrow\f$ L\*u\*v\* transformation. For example, if you have a
    .   32-bit floating-point image directly converted from an 8-bit image without any scaling, then it will
    .   have the 0..255 value range instead of 0..1 assumed by the function. So, before calling cvtColor ,
    .   you need first to scale the image down:
    .   @code
    .   img *= 1./255;
    .   cvtColor(img, img, COLOR_BGR2Luv);
    .   @endcode
    .   If you use cvtColor with 8-bit images, the conversion will have some information lost. For many
    .   applications, this will not be noticeable but it is recommended to use 32-bit images in applications
    .   that need the full range of colors or that convert an image before an operation and then convert
    .   back.
    .   
    .   If conversion adds the alpha channel, its value will set to the maximum of corresponding channel
    .   range: 255 for CV_8U, 65535 for CV_16U, 1 for CV_32F.
    .   
    .   @param src input image: 8-bit unsigned, 16-bit unsigned ( CV_16UC... ), or single-precision
    .   floating-point.
    .   @param dst output image of the same size and depth as src.
    .   @param code color space conversion code (see cv::ColorConversionCodes).
    .   @param dstCn number of channels in the destination image; if the parameter is 0, the number of the
    .   channels is derived automatically from src and code.
    .   
    .   @see @ref imgproc_color_conversions
    """
    pass

def dct(src, dst=None, flags=None): # real signature unknown; restored from __doc__
    """
    dct(src[, dst[, flags]]) -> dst
    .   @brief Performs a forward or inverse discrete Cosine transform of 1D or 2D array.
    .   
    .   The function cv::dct performs a forward or inverse discrete Cosine transform (DCT) of a 1D or 2D
    .   floating-point array:
    .   -   Forward Cosine transform of a 1D vector of N elements:
    .   \f[Y = C^{(N)}  \cdot X\f]
    .   where
    .   \f[C^{(N)}_{jk}= \sqrt{\alpha_j/N} \cos \left ( \frac{\pi(2k+1)j}{2N} \right )\f]
    .   and
    .   \f$\alpha_0=1\f$, \f$\alpha_j=2\f$ for *j \> 0*.
    .   -   Inverse Cosine transform of a 1D vector of N elements:
    .   \f[X =  \left (C^{(N)} \right )^{-1}  \cdot Y =  \left (C^{(N)} \right )^T  \cdot Y\f]
    .   (since \f$C^{(N)}\f$ is an orthogonal matrix, \f$C^{(N)} \cdot \left(C^{(N)}\right)^T = I\f$ )
    .   -   Forward 2D Cosine transform of M x N matrix:
    .   \f[Y = C^{(N)}  \cdot X  \cdot \left (C^{(N)} \right )^T\f]
    .   -   Inverse 2D Cosine transform of M x N matrix:
    .   \f[X =  \left (C^{(N)} \right )^T  \cdot X  \cdot C^{(N)}\f]
    .   
    .   The function chooses the mode of operation by looking at the flags and size of the input array:
    .   -   If (flags & DCT_INVERSE) == 0 , the function does a forward 1D or 2D transform. Otherwise, it
    .   is an inverse 1D or 2D transform.
    .   -   If (flags & DCT_ROWS) != 0 , the function performs a 1D transform of each row.
    .   -   If the array is a single column or a single row, the function performs a 1D transform.
    .   -   If none of the above is true, the function performs a 2D transform.
    .   
    .   @note Currently dct supports even-size arrays (2, 4, 6 ...). For data analysis and approximation, you
    .   can pad the array when necessary.
    .   Also, the function performance depends very much, and not monotonically, on the array size (see
    .   getOptimalDFTSize ). In the current implementation DCT of a vector of size N is calculated via DFT
    .   of a vector of size N/2 . Thus, the optimal DCT size N1 \>= N can be calculated as:
    .   @code
    .   size_t getOptimalDCTSize(size_t N) { return 2*getOptimalDFTSize((N+1)/2); }
    .   N1 = getOptimalDCTSize(N);
    .   @endcode
    .   @param src input floating-point array.
    .   @param dst output array of the same size and type as src .
    .   @param flags transformation flags as a combination of cv::DftFlags (DCT_*)
    .   @sa dft , getOptimalDFTSize , idct
    """
    pass

def decolor(src, grayscale=None, color_boost=None): # real signature unknown; restored from __doc__
    """
    decolor(src[, grayscale[, color_boost]]) -> grayscale, color_boost
    .   @brief Transforms a color image to a grayscale image. It is a basic tool in digital printing, stylized
    .   black-and-white photograph rendering, and in many single channel image processing applications
    .   @cite CL12 .
    .   
    .   @param src Input 8-bit 3-channel image.
    .   @param grayscale Output 8-bit 1-channel image.
    .   @param color_boost Output 8-bit 3-channel image.
    .   
    .   This function is to be applied on color images.
    """
    pass

def decomposeEssentialMat(E, R1=None, R2=None, t=None): # real signature unknown; restored from __doc__
    """
    decomposeEssentialMat(E[, R1[, R2[, t]]]) -> R1, R2, t
    .   @brief Decompose an essential matrix to possible rotations and translation.
    .   
    .   @param E The input essential matrix.
    .   @param R1 One possible rotation matrix.
    .   @param R2 Another possible rotation matrix.
    .   @param t One possible translation.
    .   
    .   This function decompose an essential matrix E using svd decomposition @cite HartleyZ00 . Generally 4
    .   possible poses exists for a given E. They are \f$[R_1, t]\f$, \f$[R_1, -t]\f$, \f$[R_2, t]\f$, \f$[R_2, -t]\f$. By
    .   decomposing E, you can only get the direction of the translation, so the function returns unit t.
    """
    pass

def decomposeHomographyMat(H, K, rotations=None, translations=None, normals=None): # real signature unknown; restored from __doc__
    """
    decomposeHomographyMat(H, K[, rotations[, translations[, normals]]]) -> retval, rotations, translations, normals
    .   @brief Decompose a homography matrix to rotation(s), translation(s) and plane normal(s).
    .   
    .   @param H The input homography matrix between two images.
    .   @param K The input intrinsic camera calibration matrix.
    .   @param rotations Array of rotation matrices.
    .   @param translations Array of translation matrices.
    .   @param normals Array of plane normal matrices.
    .   
    .   This function extracts relative camera motion between two views observing a planar object from the
    .   homography H induced by the plane. The intrinsic camera matrix K must also be provided. The function
    .   may return up to four mathematical solution sets. At least two of the solutions may further be
    .   invalidated if point correspondences are available by applying positive depth constraint (all points
    .   must be in front of the camera). The decomposition method is described in detail in @cite Malis .
    """
    pass

def decomposeProjectionMatrix(projMatrix, cameraMatrix=None, rotMatrix=None, transVect=None, rotMatrixX=None, rotMatrixY=None, rotMatrixZ=None, eulerAngles=None): # real signature unknown; restored from __doc__
    """
    decomposeProjectionMatrix(projMatrix[, cameraMatrix[, rotMatrix[, transVect[, rotMatrixX[, rotMatrixY[, rotMatrixZ[, eulerAngles]]]]]]]) -> cameraMatrix, rotMatrix, transVect, rotMatrixX, rotMatrixY, rotMatrixZ, eulerAngles
    .   @brief Decomposes a projection matrix into a rotation matrix and a camera matrix.
    .   
    .   @param projMatrix 3x4 input projection matrix P.
    .   @param cameraMatrix Output 3x3 camera matrix K.
    .   @param rotMatrix Output 3x3 external rotation matrix R.
    .   @param transVect Output 4x1 translation vector T.
    .   @param rotMatrixX Optional 3x3 rotation matrix around x-axis.
    .   @param rotMatrixY Optional 3x3 rotation matrix around y-axis.
    .   @param rotMatrixZ Optional 3x3 rotation matrix around z-axis.
    .   @param eulerAngles Optional three-element vector containing three Euler angles of rotation in
    .   degrees.
    .   
    .   The function computes a decomposition of a projection matrix into a calibration and a rotation
    .   matrix and the position of a camera.
    .   
    .   It optionally returns three rotation matrices, one for each axis, and three Euler angles that could
    .   be used in OpenGL. Note, there is always more than one sequence of rotations about the three
    .   principal axes that results in the same orientation of an object, eg. see @cite Slabaugh . Returned
    .   tree rotation matrices and corresponding three Euler angules are only one of the possible solutions.
    .   
    .   The function is based on RQDecomp3x3 .
    """
    pass

def demosaicing(_src, code, _dst=None, dcn=None): # real signature unknown; restored from __doc__
    """
    demosaicing(_src, code[, _dst[, dcn]]) -> _dst
    .
    """
    pass

def denoise_TVL1(observations, result, lambda=None, niters=None): # real signature unknown; restored from __doc__
    """
    denoise_TVL1(observations, result[, lambda[, niters]]) -> None
    .   @brief Primal-dual algorithm is an algorithm for solving special types of variational problems (that is,
    .   finding a function to minimize some functional). As the image denoising, in particular, may be seen
    .   as the variational problem, primal-dual algorithm then can be used to perform denoising and this is
    .   exactly what is implemented.
    .   
    .   It should be noted, that this implementation was taken from the July 2013 blog entry
    .   @cite MA13 , which also contained (slightly more general) ready-to-use source code on Python.
    .   Subsequently, that code was rewritten on C++ with the usage of openCV by Vadim Pisarevsky at the end
    .   of July 2013 and finally it was slightly adapted by later authors.
    .   
    .   Although the thorough discussion and justification of the algorithm involved may be found in
    .   @cite ChambolleEtAl, it might make sense to skim over it here, following @cite MA13 . To begin
    .   with, we consider the 1-byte gray-level images as the functions from the rectangular domain of
    .   pixels (it may be seen as set
    .   \f$\left\{(x,y)\in\mathbb{N}\times\mathbb{N}\mid 1\leq x\leq n,\;1\leq y\leq m\right\}\f$ for some
    .   \f$m,\;n\in\mathbb{N}\f$) into \f$\{0,1,\dots,255\}\f$. We shall denote the noised images as \f$f_i\f$ and with
    .   this view, given some image \f$x\f$ of the same size, we may measure how bad it is by the formula
    .   
    .   \f[\left\|\left\|\nabla x\right\|\right\| + \lambda\sum_i\left\|\left\|x-f_i\right\|\right\|\f]
    .   
    .   \f$\|\|\cdot\|\|\f$ here denotes \f$L_2\f$-norm and as you see, the first addend states that we want our
    .   image to be smooth (ideally, having zero gradient, thus being constant) and the second states that
    .   we want our result to be close to the observations we've got. If we treat \f$x\f$ as a function, this is
    .   exactly the functional what we seek to minimize and here the Primal-Dual algorithm comes into play.
    .   
    .   @param observations This array should contain one or more noised versions of the image that is to
    .   be restored.
    .   @param result Here the denoised image will be stored. There is no need to do pre-allocation of
    .   storage space, as it will be automatically allocated, if necessary.
    .   @param lambda Corresponds to \f$\lambda\f$ in the formulas above. As it is enlarged, the smooth
    .   (blurred) images are treated more favorably than detailed (but maybe more noised) ones. Roughly
    .   speaking, as it becomes smaller, the result will be more blur but more sever outliers will be
    .   removed.
    .   @param niters Number of iterations that the algorithm will run. Of course, as more iterations as
    .   better, but it is hard to quantitatively refine this statement, so just use the default and
    .   increase it if the results are poor.
    """
    pass

def DescriptorMatcher_create(descriptorMatcherType): # real signature unknown; restored from __doc__
    """
    DescriptorMatcher_create(descriptorMatcherType) -> retval
    .   @brief Creates a descriptor matcher of a given type with the default parameters (using default
    .   constructor).
    .   
    .   @param descriptorMatcherType Descriptor matcher type. Now the following matcher types are
    .   supported:
    .   -   `BruteForce` (it uses L2 )
    .   -   `BruteForce-L1`
    .   -   `BruteForce-Hamming`
    .   -   `BruteForce-Hamming(2)`
    .   -   `FlannBased`
    
    
    
    DescriptorMatcher_create(matcherType) -> retval
    .
    """
    pass

def destroyAllWindows(): # real signature unknown; restored from __doc__
    """
    destroyAllWindows() -> None
    .   @brief Destroys all of the HighGUI windows.
    .   
    .   The function destroyAllWindows destroys all of the opened HighGUI windows.
    """
    pass

def destroyWindow(winname): # real signature unknown; restored from __doc__
    """
    destroyWindow(winname) -> None
    .   @brief Destroys the specified window.
    .   
    .   The function destroyWindow destroys the window with the given name.
    .   
    .   @param winname Name of the window to be destroyed.
    """
    pass

def detailEnhance(src, dst=None, sigma_s=None, sigma_r=None): # real signature unknown; restored from __doc__
    """
    detailEnhance(src[, dst[, sigma_s[, sigma_r]]]) -> dst
    .   @brief This filter enhances the details of a particular image.
    .   
    .   @param src Input 8-bit 3-channel image.
    .   @param dst Output image with the same size and type as src.
    .   @param sigma_s Range between 0 to 200.
    .   @param sigma_r Range between 0 to 1.
    """
    pass

def determinant(mtx): # real signature unknown; restored from __doc__
    """
    determinant(mtx) -> retval
    .   @brief Returns the determinant of a square floating-point matrix.
    .   
    .   The function cv::determinant calculates and returns the determinant of the
    .   specified matrix. For small matrices ( mtx.cols=mtx.rows\<=3 ), the
    .   direct method is used. For larger matrices, the function uses LU
    .   factorization with partial pivoting.
    .   
    .   For symmetric positively-determined matrices, it is also possible to use
    .   eigen decomposition to calculate the determinant.
    .   @param mtx input matrix that must have CV_32FC1 or CV_64FC1 type and
    .   square size.
    .   @sa trace, invert, solve, eigen, @ref MatrixExpressions
    """
    pass

def dft(src, dst=None, flags=None, nonzeroRows=None): # real signature unknown; restored from __doc__
    """
    dft(src[, dst[, flags[, nonzeroRows]]]) -> dst
    .   @brief Performs a forward or inverse Discrete Fourier transform of a 1D or 2D floating-point array.
    .   
    .   The function cv::dft performs one of the following:
    .   -   Forward the Fourier transform of a 1D vector of N elements:
    .   \f[Y = F^{(N)}  \cdot X,\f]
    .   where \f$F^{(N)}_{jk}=\exp(-2\pi i j k/N)\f$ and \f$i=\sqrt{-1}\f$
    .   -   Inverse the Fourier transform of a 1D vector of N elements:
    .   \f[\begin{array}{l} X'=  \left (F^{(N)} \right )^{-1}  \cdot Y =  \left (F^{(N)} \right )^*  \cdot y  \\ X = (1/N)  \cdot X, \end{array}\f]
    .   where \f$F^*=\left(\textrm{Re}(F^{(N)})-\textrm{Im}(F^{(N)})\right)^T\f$
    .   -   Forward the 2D Fourier transform of a M x N matrix:
    .   \f[Y = F^{(M)}  \cdot X  \cdot F^{(N)}\f]
    .   -   Inverse the 2D Fourier transform of a M x N matrix:
    .   \f[\begin{array}{l} X'=  \left (F^{(M)} \right )^*  \cdot Y  \cdot \left (F^{(N)} \right )^* \\ X =  \frac{1}{M \cdot N} \cdot X' \end{array}\f]
    .   
    .   In case of real (single-channel) data, the output spectrum of the forward Fourier transform or input
    .   spectrum of the inverse Fourier transform can be represented in a packed format called *CCS*
    .   (complex-conjugate-symmetrical). It was borrowed from IPL (Intel\* Image Processing Library). Here
    .   is how 2D *CCS* spectrum looks:
    .   \f[\begin{bmatrix} Re Y_{0,0} & Re Y_{0,1} & Im Y_{0,1} & Re Y_{0,2} & Im Y_{0,2} &  \cdots & Re Y_{0,N/2-1} & Im Y_{0,N/2-1} & Re Y_{0,N/2}  \\ Re Y_{1,0} & Re Y_{1,1} & Im Y_{1,1} & Re Y_{1,2} & Im Y_{1,2} &  \cdots & Re Y_{1,N/2-1} & Im Y_{1,N/2-1} & Re Y_{1,N/2}  \\ Im Y_{1,0} & Re Y_{2,1} & Im Y_{2,1} & Re Y_{2,2} & Im Y_{2,2} &  \cdots & Re Y_{2,N/2-1} & Im Y_{2,N/2-1} & Im Y_{1,N/2}  \\ \hdotsfor{9} \\ Re Y_{M/2-1,0} &  Re Y_{M-3,1}  & Im Y_{M-3,1} &  \hdotsfor{3} & Re Y_{M-3,N/2-1} & Im Y_{M-3,N/2-1}& Re Y_{M/2-1,N/2}  \\ Im Y_{M/2-1,0} &  Re Y_{M-2,1}  & Im Y_{M-2,1} &  \hdotsfor{3} & Re Y_{M-2,N/2-1} & Im Y_{M-2,N/2-1}& Im Y_{M/2-1,N/2}  \\ Re Y_{M/2,0}  &  Re Y_{M-1,1} &  Im Y_{M-1,1} &  \hdotsfor{3} & Re Y_{M-1,N/2-1} & Im Y_{M-1,N/2-1}& Re Y_{M/2,N/2} \end{bmatrix}\f]
    .   
    .   In case of 1D transform of a real vector, the output looks like the first row of the matrix above.
    .   
    .   So, the function chooses an operation mode depending on the flags and size of the input array:
    .   -   If DFT_ROWS is set or the input array has a single row or single column, the function
    .   performs a 1D forward or inverse transform of each row of a matrix when DFT_ROWS is set.
    .   Otherwise, it performs a 2D transform.
    .   -   If the input array is real and DFT_INVERSE is not set, the function performs a forward 1D or
    .   2D transform:
    .   -   When DFT_COMPLEX_OUTPUT is set, the output is a complex matrix of the same size as
    .   input.
    .   -   When DFT_COMPLEX_OUTPUT is not set, the output is a real matrix of the same size as
    .   input. In case of 2D transform, it uses the packed format as shown above. In case of a
    .   single 1D transform, it looks like the first row of the matrix above. In case of
    .   multiple 1D transforms (when using the DFT_ROWS flag), each row of the output matrix
    .   looks like the first row of the matrix above.
    .   -   If the input array is complex and either DFT_INVERSE or DFT_REAL_OUTPUT are not set, the
    .   output is a complex array of the same size as input. The function performs a forward or
    .   inverse 1D or 2D transform of the whole input array or each row of the input array
    .   independently, depending on the flags DFT_INVERSE and DFT_ROWS.
    .   -   When DFT_INVERSE is set and the input array is real, or it is complex but DFT_REAL_OUTPUT
    .   is set, the output is a real array of the same size as input. The function performs a 1D or 2D
    .   inverse transformation of the whole input array or each individual row, depending on the flags
    .   DFT_INVERSE and DFT_ROWS.
    .   
    .   If DFT_SCALE is set, the scaling is done after the transformation.
    .   
    .   Unlike dct , the function supports arrays of arbitrary size. But only those arrays are processed
    .   efficiently, whose sizes can be factorized in a product of small prime numbers (2, 3, and 5 in the
    .   current implementation). Such an efficient DFT size can be calculated using the getOptimalDFTSize
    .   method.
    .   
    .   The sample below illustrates how to calculate a DFT-based convolution of two 2D real arrays:
    .   @code
    .   void convolveDFT(InputArray A, InputArray B, OutputArray C)
    .   {
    .   // reallocate the output array if needed
    .   C.create(abs(A.rows - B.rows)+1, abs(A.cols - B.cols)+1, A.type());
    .   Size dftSize;
    .   // calculate the size of DFT transform
    .   dftSize.width = getOptimalDFTSize(A.cols + B.cols - 1);
    .   dftSize.height = getOptimalDFTSize(A.rows + B.rows - 1);
    .   
    .   // allocate temporary buffers and initialize them with 0's
    .   Mat tempA(dftSize, A.type(), Scalar::all(0));
    .   Mat tempB(dftSize, B.type(), Scalar::all(0));
    .   
    .   // copy A and B to the top-left corners of tempA and tempB, respectively
    .   Mat roiA(tempA, Rect(0,0,A.cols,A.rows));
    .   A.copyTo(roiA);
    .   Mat roiB(tempB, Rect(0,0,B.cols,B.rows));
    .   B.copyTo(roiB);
    .   
    .   // now transform the padded A & B in-place;
    .   // use "nonzeroRows" hint for faster processing
    .   dft(tempA, tempA, 0, A.rows);
    .   dft(tempB, tempB, 0, B.rows);
    .   
    .   // multiply the spectrums;
    .   // the function handles packed spectrum representations well
    .   mulSpectrums(tempA, tempB, tempA);
    .   
    .   // transform the product back from the frequency domain.
    .   // Even though all the result rows will be non-zero,
    .   // you need only the first C.rows of them, and thus you
    .   // pass nonzeroRows == C.rows
    .   dft(tempA, tempA, DFT_INVERSE + DFT_SCALE, C.rows);
    .   
    .   // now copy the result back to C.
    .   tempA(Rect(0, 0, C.cols, C.rows)).copyTo(C);
    .   
    .   // all the temporary buffers will be deallocated automatically
    .   }
    .   @endcode
    .   To optimize this sample, consider the following approaches:
    .   -   Since nonzeroRows != 0 is passed to the forward transform calls and since A and B are copied to
    .   the top-left corners of tempA and tempB, respectively, it is not necessary to clear the whole
    .   tempA and tempB. It is only necessary to clear the tempA.cols - A.cols ( tempB.cols - B.cols)
    .   rightmost columns of the matrices.
    .   -   This DFT-based convolution does not have to be applied to the whole big arrays, especially if B
    .   is significantly smaller than A or vice versa. Instead, you can calculate convolution by parts.
    .   To do this, you need to split the output array C into multiple tiles. For each tile, estimate
    .   which parts of A and B are required to calculate convolution in this tile. If the tiles in C are
    .   too small, the speed will decrease a lot because of repeated work. In the ultimate case, when
    .   each tile in C is a single pixel, the algorithm becomes equivalent to the naive convolution
    .   algorithm. If the tiles are too big, the temporary arrays tempA and tempB become too big and
    .   there is also a slowdown because of bad cache locality. So, there is an optimal tile size
    .   somewhere in the middle.
    .   -   If different tiles in C can be calculated in parallel and, thus, the convolution is done by
    .   parts, the loop can be threaded.
    .   
    .   All of the above improvements have been implemented in matchTemplate and filter2D . Therefore, by
    .   using them, you can get the performance even better than with the above theoretically optimal
    .   implementation. Though, those two functions actually calculate cross-correlation, not convolution,
    .   so you need to "flip" the second convolution operand B vertically and horizontally using flip .
    .   @note
    .   -   An example using the discrete fourier transform can be found at
    .   opencv_source_code/samples/cpp/dft.cpp
    .   -   (Python) An example using the dft functionality to perform Wiener deconvolution can be found
    .   at opencv_source/samples/python/deconvolution.py
    .   -   (Python) An example rearranging the quadrants of a Fourier image can be found at
    .   opencv_source/samples/python/dft.py
    .   @param src input array that could be real or complex.
    .   @param dst output array whose size and type depends on the flags .
    .   @param flags transformation flags, representing a combination of the cv::DftFlags
    .   @param nonzeroRows when the parameter is not zero, the function assumes that only the first
    .   nonzeroRows rows of the input array (DFT_INVERSE is not set) or only the first nonzeroRows of the
    .   output array (DFT_INVERSE is set) contain non-zeros, thus, the function can handle the rest of the
    .   rows more efficiently and save some time; this technique is very useful for calculating array
    .   cross-correlation or convolution using DFT.
    .   @sa dct , getOptimalDFTSize , mulSpectrums, filter2D , matchTemplate , flip , cartToPolar ,
    .   magnitude , phase
    """
    pass

def dilate(src, kernel, dst=None, anchor=None, iterations=None, borderType=None, borderValue=None): # real signature unknown; restored from __doc__
    """
    dilate(src, kernel[, dst[, anchor[, iterations[, borderType[, borderValue]]]]]) -> dst
    .   @brief Dilates an image by using a specific structuring element.
    .   
    .   The function dilates the source image using the specified structuring element that determines the
    .   shape of a pixel neighborhood over which the maximum is taken:
    .   \f[\texttt{dst} (x,y) =  \max _{(x',y'):  \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\f]
    .   
    .   The function supports the in-place mode. Dilation can be applied several ( iterations ) times. In
    .   case of multi-channel images, each channel is processed independently.
    .   
    .   @param src input image; the number of channels can be arbitrary, but the depth should be one of
    .   CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
    .   @param dst output image of the same size and type as src\`.
    .   @param kernel structuring element used for dilation; if elemenat=Mat(), a 3 x 3 rectangular
    .   structuring element is used. Kernel can be created using getStructuringElement
    .   @param anchor position of the anchor within the element; default value (-1, -1) means that the
    .   anchor is at the element center.
    .   @param iterations number of times dilation is applied.
    .   @param borderType pixel extrapolation method, see cv::BorderTypes
    .   @param borderValue border value in case of a constant border
    .   @sa  erode, morphologyEx, getStructuringElement
    """
    pass

def displayOverlay(winname, text, delayms=None): # real signature unknown; restored from __doc__
    """
    displayOverlay(winname, text[, delayms]) -> None
    .   @brief Displays a text on a window image as an overlay for a specified duration.
    .   
    .   The function displayOverlay displays useful information/tips on top of the window for a certain
    .   amount of time *delayms*. The function does not modify the image, displayed in the window, that is,
    .   after the specified delay the original content of the window is restored.
    .   
    .   @param winname Name of the window.
    .   @param text Overlay text to write on a window image.
    .   @param delayms The period (in milliseconds), during which the overlay text is displayed. If this
    .   function is called before the previous overlay text timed out, the timer is restarted and the text
    .   is updated. If this value is zero, the text never disappears.
    """
    pass

def displayStatusBar(winname, text, delayms=None): # real signature unknown; restored from __doc__
    """
    displayStatusBar(winname, text[, delayms]) -> None
    .   @brief Displays a text on the window statusbar during the specified period of time.
    .   
    .   The function displayStatusBar displays useful information/tips on top of the window for a certain
    .   amount of time *delayms* . This information is displayed on the window statusbar (the window must be
    .   created with the CV_GUI_EXPANDED flags).
    .   
    .   @param winname Name of the window.
    .   @param text Text to write on the window statusbar.
    .   @param delayms Duration (in milliseconds) to display the text. If this function is called before
    .   the previous text timed out, the timer is restarted and the text is updated. If this value is
    .   zero, the text never disappears.
    """
    pass

def distanceTransform(src, distanceType, maskSize, dst=None, dstType=None): # real signature unknown; restored from __doc__
    """
    distanceTransform(src, distanceType, maskSize[, dst[, dstType]]) -> dst
    .   @overload
    .   @param src 8-bit, single-channel (binary) source image.
    .   @param dst Output image with calculated distances. It is a 8-bit or 32-bit floating-point,
    .   single-channel image of the same size as src .
    .   @param distanceType Type of distance, see cv::DistanceTypes
    .   @param maskSize Size of the distance transform mask, see cv::DistanceTransformMasks. In case of the
    .   DIST_L1 or DIST_C distance type, the parameter is forced to 3 because a \f$3\times 3\f$ mask gives
    .   the same result as \f$5\times 5\f$ or any larger aperture.
    .   @param dstType Type of output image. It can be CV_8U or CV_32F. Type CV_8U can be used only for
    .   the first variant of the function and distanceType == DIST_L1.
    """
    pass

def distanceTransformWithLabels(src, distanceType, maskSize, dst=None, labels=None, labelType=None): # real signature unknown; restored from __doc__
    """
    distanceTransformWithLabels(src, distanceType, maskSize[, dst[, labels[, labelType]]]) -> dst, labels
    .   @brief Calculates the distance to the closest zero pixel for each pixel of the source image.
    .   
    .   The function cv::distanceTransform calculates the approximate or precise distance from every binary
    .   image pixel to the nearest zero pixel. For zero image pixels, the distance will obviously be zero.
    .   
    .   When maskSize == DIST_MASK_PRECISE and distanceType == DIST_L2 , the function runs the
    .   algorithm described in @cite Felzenszwalb04 . This algorithm is parallelized with the TBB library.
    .   
    .   In other cases, the algorithm @cite Borgefors86 is used. This means that for a pixel the function
    .   finds the shortest path to the nearest zero pixel consisting of basic shifts: horizontal, vertical,
    .   diagonal, or knight's move (the latest is available for a \f$5\times 5\f$ mask). The overall
    .   distance is calculated as a sum of these basic distances. Since the distance function should be
    .   symmetric, all of the horizontal and vertical shifts must have the same cost (denoted as a ), all
    .   the diagonal shifts must have the same cost (denoted as `b`), and all knight's moves must have the
    .   same cost (denoted as `c`). For the cv::DIST_C and cv::DIST_L1 types, the distance is calculated
    .   precisely, whereas for cv::DIST_L2 (Euclidean distance) the distance can be calculated only with a
    .   relative error (a \f$5\times 5\f$ mask gives more accurate results). For `a`,`b`, and `c`, OpenCV
    .   uses the values suggested in the original paper:
    .   - DIST_L1: `a = 1, b = 2`
    .   - DIST_L2:
    .   - `3 x 3`: `a=0.955, b=1.3693`
    .   - `5 x 5`: `a=1, b=1.4, c=2.1969`
    .   - DIST_C: `a = 1, b = 1`
    .   
    .   Typically, for a fast, coarse distance estimation DIST_L2, a \f$3\times 3\f$ mask is used. For a
    .   more accurate distance estimation DIST_L2, a \f$5\times 5\f$ mask or the precise algorithm is used.
    .   Note that both the precise and the approximate algorithms are linear on the number of pixels.
    .   
    .   This variant of the function does not only compute the minimum distance for each pixel \f$(x, y)\f$
    .   but also identifies the nearest connected component consisting of zero pixels
    .   (labelType==DIST_LABEL_CCOMP) or the nearest zero pixel (labelType==DIST_LABEL_PIXEL). Index of the
    .   component/pixel is stored in `labels(x, y)`. When labelType==DIST_LABEL_CCOMP, the function
    .   automatically finds connected components of zero pixels in the input image and marks them with
    .   distinct labels. When labelType==DIST_LABEL_CCOMP, the function scans through the input image and
    .   marks all the zero pixels with distinct labels.
    .   
    .   In this mode, the complexity is still linear. That is, the function provides a very fast way to
    .   compute the Voronoi diagram for a binary image. Currently, the second variant can use only the
    .   approximate distance transform algorithm, i.e. maskSize=DIST_MASK_PRECISE is not supported
    .   yet.
    .   
    .   @param src 8-bit, single-channel (binary) source image.
    .   @param dst Output image with calculated distances. It is a 8-bit or 32-bit floating-point,
    .   single-channel image of the same size as src.
    .   @param labels Output 2D array of labels (the discrete Voronoi diagram). It has the type
    .   CV_32SC1 and the same size as src.
    .   @param distanceType Type of distance, see cv::DistanceTypes
    .   @param maskSize Size of the distance transform mask, see cv::DistanceTransformMasks.
    .   DIST_MASK_PRECISE is not supported by this variant. In case of the DIST_L1 or DIST_C distance type,
    .   the parameter is forced to 3 because a \f$3\times 3\f$ mask gives the same result as \f$5\times
    .   5\f$ or any larger aperture.
    .   @param labelType Type of the label array to build, see cv::DistanceTransformLabelTypes.
    """
    pass

def divide(src1, src2, dst=None, scale=None, dtype=None): # real signature unknown; restored from __doc__
    """
    divide(src1, src2[, dst[, scale[, dtype]]]) -> dst
    .   @brief Performs per-element division of two arrays or a scalar by an array.
    .   
    .   The function cv::divide divides one array by another:
    .   \f[\texttt{dst(I) = saturate(src1(I)*scale/src2(I))}\f]
    .   or a scalar by an array when there is no src1 :
    .   \f[\texttt{dst(I) = saturate(scale/src2(I))}\f]
    .   
    .   When src2(I) is zero, dst(I) will also be zero. Different channels of
    .   multi-channel arrays are processed independently.
    .   
    .   @note Saturation is not applied when the output array has the depth CV_32S. You may even get
    .   result of an incorrect sign in the case of overflow.
    .   @param src1 first input array.
    .   @param src2 second input array of the same size and type as src1.
    .   @param scale scalar factor.
    .   @param dst output array of the same size and type as src2.
    .   @param dtype optional depth of the output array; if -1, dst will have depth src2.depth(), but in
    .   case of an array-by-array division, you can only pass -1 when src1.depth()==src2.depth().
    .   @sa  multiply, add, subtract
    
    
    
    divide(scale, src2[, dst[, dtype]]) -> dst
    .   @overload
    """
    pass

def DMatch(): # real signature unknown; restored from __doc__
    """
    DMatch() -> <DMatch object>
    .   
    
    
    
    DMatch(_queryIdx, _trainIdx, _distance) -> <DMatch object>
    .   
    
    
    
    DMatch(_queryIdx, _trainIdx, _imgIdx, _distance) -> <DMatch object>
    .
    """
    pass

def drawChessboardCorners(image, patternSize, corners, patternWasFound): # real signature unknown; restored from __doc__
    """
    drawChessboardCorners(image, patternSize, corners, patternWasFound) -> image
    .   @brief Renders the detected chessboard corners.
    .   
    .   @param image Destination image. It must be an 8-bit color image.
    .   @param patternSize Number of inner corners per a chessboard row and column
    .   (patternSize = cv::Size(points_per_row,points_per_column)).
    .   @param corners Array of detected corners, the output of findChessboardCorners.
    .   @param patternWasFound Parameter indicating whether the complete board was found or not. The
    .   return value of findChessboardCorners should be passed here.
    .   
    .   The function draws individual chessboard corners detected either as red circles if the board was not
    .   found, or as colored corners connected with lines if the board was found.
    """
    pass

def drawContours(image, contours, contourIdx, color, thickness=None, lineType=None, hierarchy=None, maxLevel=None, offset=None): # real signature unknown; restored from __doc__
    """
    drawContours(image, contours, contourIdx, color[, thickness[, lineType[, hierarchy[, maxLevel[, offset]]]]]) -> image
    .   @brief Draws contours outlines or filled contours.
    .   
    .   The function draws contour outlines in the image if \f$\texttt{thickness} \ge 0\f$ or fills the area
    .   bounded by the contours if \f$\texttt{thickness}<0\f$ . The example below shows how to retrieve
    .   connected components from the binary image and label them: :
    .   @code
    .   #include "opencv2/imgproc.hpp"
    .   #include "opencv2/highgui.hpp"
    .   
    .   using namespace cv;
    .   using namespace std;
    .   
    .   int main( int argc, char** argv )
    .   {
    .   Mat src;
    .   // the first command-line parameter must be a filename of the binary
    .   // (black-n-white) image
    .   if( argc != 2 || !(src=imread(argv[1], 0)).data)
    .   return -1;
    .   
    .   Mat dst = Mat::zeros(src.rows, src.cols, CV_8UC3);
    .   
    .   src = src > 1;
    .   namedWindow( "Source", 1 );
    .   imshow( "Source", src );
    .   
    .   vector<vector<Point> > contours;
    .   vector<Vec4i> hierarchy;
    .   
    .   findContours( src, contours, hierarchy,
    .   RETR_CCOMP, CHAIN_APPROX_SIMPLE );
    .   
    .   // iterate through all the top-level contours,
    .   // draw each connected component with its own random color
    .   int idx = 0;
    .   for( ; idx >= 0; idx = hierarchy[idx][0] )
    .   {
    .   Scalar color( rand()&255, rand()&255, rand()&255 );
    .   drawContours( dst, contours, idx, color, FILLED, 8, hierarchy );
    .   }
    .   
    .   namedWindow( "Components", 1 );
    .   imshow( "Components", dst );
    .   waitKey(0);
    .   }
    .   @endcode
    .   
    .   @param image Destination image.
    .   @param contours All the input contours. Each contour is stored as a point vector.
    .   @param contourIdx Parameter indicating a contour to draw. If it is negative, all the contours are drawn.
    .   @param color Color of the contours.
    .   @param thickness Thickness of lines the contours are drawn with. If it is negative (for example,
    .   thickness=CV_FILLED ), the contour interiors are drawn.
    .   @param lineType Line connectivity. See cv::LineTypes.
    .   @param hierarchy Optional information about hierarchy. It is only needed if you want to draw only
    .   some of the contours (see maxLevel ).
    .   @param maxLevel Maximal level for drawn contours. If it is 0, only the specified contour is drawn.
    .   If it is 1, the function draws the contour(s) and all the nested contours. If it is 2, the function
    .   draws the contours, all the nested contours, all the nested-to-nested contours, and so on. This
    .   parameter is only taken into account when there is hierarchy available.
    .   @param offset Optional contour shift parameter. Shift all the drawn contours by the specified
    .   \f$\texttt{offset}=(dx,dy)\f$ .
    """
    pass

def drawKeypoints(image, keypoints, outImage, color=None, flags=None): # real signature unknown; restored from __doc__
    """
    drawKeypoints(image, keypoints, outImage[, color[, flags]]) -> outImage
    .   @brief Draws keypoints.
    .   
    .   @param image Source image.
    .   @param keypoints Keypoints from the source image.
    .   @param outImage Output image. Its content depends on the flags value defining what is drawn in the
    .   output image. See possible flags bit values below.
    .   @param color Color of keypoints.
    .   @param flags Flags setting drawing features. Possible flags bit values are defined by
    .   DrawMatchesFlags. See details above in drawMatches .
    .   
    .   @note
    .   For Python API, flags are modified as cv2.DRAW_MATCHES_FLAGS_DEFAULT,
    .   cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS, cv2.DRAW_MATCHES_FLAGS_DRAW_OVER_OUTIMG,
    .   cv2.DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTS
    """
    pass

def drawMarker(img, position, color, markerType=None, markerSize=None, thickness=None, line_type=None): # real signature unknown; restored from __doc__
    """
    drawMarker(img, position, color[, markerType[, markerSize[, thickness[, line_type]]]]) -> img
    .   @brief Draws a marker on a predefined position in an image.
    .   
    .   The function drawMarker draws a marker on a given position in the image. For the moment several
    .   marker types are supported, see cv::MarkerTypes for more information.
    .   
    .   @param img Image.
    .   @param position The point where the crosshair is positioned.
    .   @param color Line color.
    .   @param markerType The specific type of marker you want to use, see cv::MarkerTypes
    .   @param thickness Line thickness.
    .   @param line_type Type of the line, see cv::LineTypes
    .   @param markerSize The length of the marker axis [default = 20 pixels]
    """
    pass

def drawMatches(img1, keypoints1, img2, keypoints2, matches1to2, outImg, matchColor=None, singlePointColor=None, matchesMask=None, flags=None): # real signature unknown; restored from __doc__
    """
    drawMatches(img1, keypoints1, img2, keypoints2, matches1to2, outImg[, matchColor[, singlePointColor[, matchesMask[, flags]]]]) -> outImg
    .   @brief Draws the found matches of keypoints from two images.
    .   
    .   @param img1 First source image.
    .   @param keypoints1 Keypoints from the first source image.
    .   @param img2 Second source image.
    .   @param keypoints2 Keypoints from the second source image.
    .   @param matches1to2 Matches from the first image to the second one, which means that keypoints1[i]
    .   has a corresponding point in keypoints2[matches[i]] .
    .   @param outImg Output image. Its content depends on the flags value defining what is drawn in the
    .   output image. See possible flags bit values below.
    .   @param matchColor Color of matches (lines and connected keypoints). If matchColor==Scalar::all(-1)
    .   , the color is generated randomly.
    .   @param singlePointColor Color of single keypoints (circles), which means that keypoints do not
    .   have the matches. If singlePointColor==Scalar::all(-1) , the color is generated randomly.
    .   @param matchesMask Mask determining which matches are drawn. If the mask is empty, all matches are
    .   drawn.
    .   @param flags Flags setting drawing features. Possible flags bit values are defined by
    .   DrawMatchesFlags.
    .   
    .   This function draws matches of keypoints from two images in the output image. Match is a line
    .   connecting two keypoints (circles). See cv::DrawMatchesFlags.
    """
    pass

def drawMatchesKnn(img1, keypoints1, img2, keypoints2, matches1to2, outImg, matchColor=None, singlePointColor=None, matchesMask=None, flags=None): # real signature unknown; restored from __doc__
    """
    drawMatchesKnn(img1, keypoints1, img2, keypoints2, matches1to2, outImg[, matchColor[, singlePointColor[, matchesMask[, flags]]]]) -> outImg
    .   @overload
    """
    pass

def DualTVL1OpticalFlow_create(tau=None, lambda=None, theta=None, nscales=None, warps=None, epsilon=None, innnerIterations=None, outerIterations=None, scaleStep=None, gamma=None, medianFiltering=None, useInitialFlow=None): # real signature unknown; restored from __doc__
    """
    DualTVL1OpticalFlow_create([, tau[, lambda[, theta[, nscales[, warps[, epsilon[, innnerIterations[, outerIterations[, scaleStep[, gamma[, medianFiltering[, useInitialFlow]]]]]]]]]]]]) -> retval
    .   @brief Creates instance of cv::DualTVL1OpticalFlow
    """
    pass

def edgePreservingFilter(src, dst=None, flags=None, sigma_s=None, sigma_r=None): # real signature unknown; restored from __doc__
    """
    edgePreservingFilter(src[, dst[, flags[, sigma_s[, sigma_r]]]]) -> dst
    .   @brief Filtering is the fundamental operation in image and video processing. Edge-preserving smoothing
    .   filters are used in many different applications @cite EM11 .
    .   
    .   @param src Input 8-bit 3-channel image.
    .   @param dst Output 8-bit 3-channel image.
    .   @param flags Edge preserving filters:
    .   -   **RECURS_FILTER** = 1
    .   -   **NORMCONV_FILTER** = 2
    .   @param sigma_s Range between 0 to 200.
    .   @param sigma_r Range between 0 to 1.
    """
    pass

def eigen(src, eigenvalues=None, eigenvectors=None): # real signature unknown; restored from __doc__
    """
    eigen(src[, eigenvalues[, eigenvectors]]) -> retval, eigenvalues, eigenvectors
    .   @brief Calculates eigenvalues and eigenvectors of a symmetric matrix.
    .   
    .   The function cv::eigen calculates just eigenvalues, or eigenvalues and eigenvectors of the symmetric
    .   matrix src:
    .   @code
    .   src*eigenvectors.row(i).t() = eigenvalues.at<srcType>(i)*eigenvectors.row(i).t()
    .   @endcode
    .   @note in the new and the old interfaces different ordering of eigenvalues and eigenvectors
    .   parameters is used.
    .   @param src input matrix that must have CV_32FC1 or CV_64FC1 type, square size and be symmetrical
    .   (src ^T^ == src).
    .   @param eigenvalues output vector of eigenvalues of the same type as src; the eigenvalues are stored
    .   in the descending order.
    .   @param eigenvectors output matrix of eigenvectors; it has the same size and type as src; the
    .   eigenvectors are stored as subsequent matrix rows, in the same order as the corresponding
    .   eigenvalues.
    .   @sa completeSymm , PCA
    """
    pass

def ellipse(img, center, axes, angle, startAngle, endAngle, color, thickness=None, lineType=None, shift=None): # real signature unknown; restored from __doc__
    """
    ellipse(img, center, axes, angle, startAngle, endAngle, color[, thickness[, lineType[, shift]]]) -> img
    .   @brief Draws a simple or thick elliptic arc or fills an ellipse sector.
    .   
    .   The function cv::ellipse with more parameters draws an ellipse outline, a filled ellipse, an elliptic
    .   arc, or a filled ellipse sector. The drawing code uses general parametric form.
    .   A piecewise-linear curve is used to approximate the elliptic arc
    .   boundary. If you need more control of the ellipse rendering, you can retrieve the curve using
    .   cv::ellipse2Poly and then render it with polylines or fill it with cv::fillPoly. If you use the first
    .   variant of the function and want to draw the whole ellipse, not an arc, pass `startAngle=0` and
    .   `endAngle=360`. If `startAngle` is greater than `endAngle`, they are swapped. The figure below explains
    .   the meaning of the parameters to draw the blue arc.
    .   
    .   ![Parameters of Elliptic Arc](pics/ellipse.svg)
    .   
    .   @param img Image.
    .   @param center Center of the ellipse.
    .   @param axes Half of the size of the ellipse main axes.
    .   @param angle Ellipse rotation angle in degrees.
    .   @param startAngle Starting angle of the elliptic arc in degrees.
    .   @param endAngle Ending angle of the elliptic arc in degrees.
    .   @param color Ellipse color.
    .   @param thickness Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that
    .   a filled ellipse sector is to be drawn.
    .   @param lineType Type of the ellipse boundary. See the line description.
    .   @param shift Number of fractional bits in the coordinates of the center and values of axes.
    
    
    
    ellipse(img, box, color[, thickness[, lineType]]) -> img
    .   @overload
    .   @param img Image.
    .   @param box Alternative ellipse representation via RotatedRect. This means that the function draws
    .   an ellipse inscribed in the rotated rectangle.
    .   @param color Ellipse color.
    .   @param thickness Thickness of the ellipse arc outline, if positive. Otherwise, this indicates that
    .   a filled ellipse sector is to be drawn.
    .   @param lineType Type of the ellipse boundary. See the line description.
    """
    pass

def ellipse2Poly(center, axes, angle, arcStart, arcEnd, delta): # real signature unknown; restored from __doc__
    """
    ellipse2Poly(center, axes, angle, arcStart, arcEnd, delta) -> pts
    .   @brief Approximates an elliptic arc with a polyline.
    .   
    .   The function ellipse2Poly computes the vertices of a polyline that approximates the specified
    .   elliptic arc. It is used by cv::ellipse. If `arcStart` is greater than `arcEnd`, they are swapped.
    .   
    .   @param center Center of the arc.
    .   @param axes Half of the size of the ellipse main axes. See the ellipse for details.
    .   @param angle Rotation angle of the ellipse in degrees. See the ellipse for details.
    .   @param arcStart Starting angle of the elliptic arc in degrees.
    .   @param arcEnd Ending angle of the elliptic arc in degrees.
    .   @param delta Angle between the subsequent polyline vertices. It defines the approximation
    .   accuracy.
    .   @param pts Output vector of polyline vertices.
    """
    pass

def EMD(signature1, signature2, distType, cost=None, lowerBound=None, flow=None): # real signature unknown; restored from __doc__
    """
    EMD(signature1, signature2, distType[, cost[, lowerBound[, flow]]]) -> retval, lowerBound, flow
    .   @brief Computes the "minimal work" distance between two weighted point configurations.
    .   
    .   The function computes the earth mover distance and/or a lower boundary of the distance between the
    .   two weighted point configurations. One of the applications described in @cite RubnerSept98,
    .   @cite Rubner2000 is multi-dimensional histogram comparison for image retrieval. EMD is a transportation
    .   problem that is solved using some modification of a simplex algorithm, thus the complexity is
    .   exponential in the worst case, though, on average it is much faster. In the case of a real metric
    .   the lower boundary can be calculated even faster (using linear-time algorithm) and it can be used
    .   to determine roughly whether the two signatures are far enough so that they cannot relate to the
    .   same object.
    .   
    .   @param signature1 First signature, a \f$\texttt{size1}\times \texttt{dims}+1\f$ floating-point matrix.
    .   Each row stores the point weight followed by the point coordinates. The matrix is allowed to have
    .   a single column (weights only) if the user-defined cost matrix is used. The weights must be
    .   non-negative and have at least one non-zero value.
    .   @param signature2 Second signature of the same format as signature1 , though the number of rows
    .   may be different. The total weights may be different. In this case an extra "dummy" point is added
    .   to either signature1 or signature2. The weights must be non-negative and have at least one non-zero
    .   value.
    .   @param distType Used metric. See cv::DistanceTypes.
    .   @param cost User-defined \f$\texttt{size1}\times \texttt{size2}\f$ cost matrix. Also, if a cost matrix
    .   is used, lower boundary lowerBound cannot be calculated because it needs a metric function.
    .   @param lowerBound Optional input/output parameter: lower boundary of a distance between the two
    .   signatures that is a distance between mass centers. The lower boundary may not be calculated if
    .   the user-defined cost matrix is used, the total weights of point configurations are not equal, or
    .   if the signatures consist of weights only (the signature matrices have a single column). You
    .   **must** initialize \*lowerBound . If the calculated distance between mass centers is greater or
    .   equal to \*lowerBound (it means that the signatures are far enough), the function does not
    .   calculate EMD. In any case \*lowerBound is set to the calculated distance between mass centers on
    .   return. Thus, if you want to calculate both distance between mass centers and EMD, \*lowerBound
    .   should be set to 0.
    .   @param flow Resultant \f$\texttt{size1} \times \texttt{size2}\f$ flow matrix: \f$\texttt{flow}_{i,j}\f$ is
    .   a flow from \f$i\f$ -th point of signature1 to \f$j\f$ -th point of signature2 .
    """
    pass

def equalizeHist(src, dst=None): # real signature unknown; restored from __doc__
    """
    equalizeHist(src[, dst]) -> dst
    .   @brief Equalizes the histogram of a grayscale image.
    .   
    .   The function equalizes the histogram of the input image using the following algorithm:
    .   
    .   - Calculate the histogram \f$H\f$ for src .
    .   - Normalize the histogram so that the sum of histogram bins is 255.
    .   - Compute the integral of the histogram:
    .   \f[H'_i =  \sum _{0  \le j < i} H(j)\f]
    .   - Transform the image using \f$H'\f$ as a look-up table: \f$\texttt{dst}(x,y) = H'(\texttt{src}(x,y))\f$
    .   
    .   The algorithm normalizes the brightness and increases the contrast of the image.
    .   
    .   @param src Source 8-bit single channel image.
    .   @param dst Destination image of the same size and type as src .
    """
    pass

def erode(src, kernel, dst=None, anchor=None, iterations=None, borderType=None, borderValue=None): # real signature unknown; restored from __doc__
    """
    erode(src, kernel[, dst[, anchor[, iterations[, borderType[, borderValue]]]]]) -> dst
    .   @brief Erodes an image by using a specific structuring element.
    .   
    .   The function erodes the source image using the specified structuring element that determines the
    .   shape of a pixel neighborhood over which the minimum is taken:
    .   
    .   \f[\texttt{dst} (x,y) =  \min _{(x',y'):  \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\f]
    .   
    .   The function supports the in-place mode. Erosion can be applied several ( iterations ) times. In
    .   case of multi-channel images, each channel is processed independently.
    .   
    .   @param src input image; the number of channels can be arbitrary, but the depth should be one of
    .   CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
    .   @param dst output image of the same size and type as src.
    .   @param kernel structuring element used for erosion; if `element=Mat()`, a `3 x 3` rectangular
    .   structuring element is used. Kernel can be created using getStructuringElement.
    .   @param anchor position of the anchor within the element; default value (-1, -1) means that the
    .   anchor is at the element center.
    .   @param iterations number of times erosion is applied.
    .   @param borderType pixel extrapolation method, see cv::BorderTypes
    .   @param borderValue border value in case of a constant border
    .   @sa  dilate, morphologyEx, getStructuringElement
    """
    pass

def estimateAffine2D(from_, to, inliers=None, method=None, ransacReprojThreshold=None, maxIters=None, confidence=None, refineIters=None): # real signature unknown; restored from __doc__
    """
    estimateAffine2D(from, to[, inliers[, method[, ransacReprojThreshold[, maxIters[, confidence[, refineIters]]]]]]) -> retval, inliers
    .   @brief Computes an optimal affine transformation between two 2D point sets.
    .   
    .   @param from First input 2D point set.
    .   @param to Second input 2D point set.
    .   @param inliers Output vector indicating which points are inliers.
    .   @param method Robust method used to compute tranformation. The following methods are possible:
    .   -   cv::RANSAC - RANSAC-based robust method
    .   -   cv::LMEDS - Least-Median robust method
    .   RANSAC is the default method.
    .   @param ransacReprojThreshold Maximum reprojection error in the RANSAC algorithm to consider
    .   a point as an inlier. Applies only to RANSAC.
    .   @param maxIters The maximum number of robust method iterations, 2000 is the maximum it can be.
    .   @param confidence Confidence level, between 0 and 1, for the estimated transformation. Anything
    .   between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation
    .   significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation.
    .   @param refineIters Maximum number of iterations of refining algorithm (Levenberg-Marquardt).
    .   Passing 0 will disable refining, so the output matrix will be output of robust method.
    .   
    .   @return Output 2D affine transformation matrix \f$2 \times 3\f$ or empty matrix if transformation
    .   could not be estimated.
    .   
    .   The function estimates an optimal 2D affine transformation between two 2D point sets using the
    .   selected robust algorithm.
    .   
    .   The computed transformation is then refined further (using only inliers) with the
    .   Levenberg-Marquardt method to reduce the re-projection error even more.
    .   
    .   @note
    .   The RANSAC method can handle practically any ratio of outliers but need a threshold to
    .   distinguish inliers from outliers. The method LMeDS does not need any threshold but it works
    .   correctly only when there are more than 50% of inliers.
    .   
    .   @sa estimateAffinePartial2D, getAffineTransform
    """
    pass

def estimateAffine3D(src, dst, out=None, inliers=None, ransacThreshold=None, confidence=None): # real signature unknown; restored from __doc__
    """
    estimateAffine3D(src, dst[, out[, inliers[, ransacThreshold[, confidence]]]]) -> retval, out, inliers
    .   @brief Computes an optimal affine transformation between two 3D point sets.
    .   
    .   @param src First input 3D point set.
    .   @param dst Second input 3D point set.
    .   @param out Output 3D affine transformation matrix \f$3 \times 4\f$ .
    .   @param inliers Output vector indicating which points are inliers.
    .   @param ransacThreshold Maximum reprojection error in the RANSAC algorithm to consider a point as
    .   an inlier.
    .   @param confidence Confidence level, between 0 and 1, for the estimated transformation. Anything
    .   between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation
    .   significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation.
    .   
    .   The function estimates an optimal 3D affine transformation between two 3D point sets using the
    .   RANSAC algorithm.
    """
    pass

def estimateAffinePartial2D(from_, to, inliers=None, method=None, ransacReprojThreshold=None, maxIters=None, confidence=None, refineIters=None): # real signature unknown; restored from __doc__
    """
    estimateAffinePartial2D(from, to[, inliers[, method[, ransacReprojThreshold[, maxIters[, confidence[, refineIters]]]]]]) -> retval, inliers
    .   @brief Computes an optimal limited affine transformation with 4 degrees of freedom between
    .   two 2D point sets.
    .   
    .   @param from First input 2D point set.
    .   @param to Second input 2D point set.
    .   @param inliers Output vector indicating which points are inliers.
    .   @param method Robust method used to compute tranformation. The following methods are possible:
    .   -   cv::RANSAC - RANSAC-based robust method
    .   -   cv::LMEDS - Least-Median robust method
    .   RANSAC is the default method.
    .   @param ransacReprojThreshold Maximum reprojection error in the RANSAC algorithm to consider
    .   a point as an inlier. Applies only to RANSAC.
    .   @param maxIters The maximum number of robust method iterations, 2000 is the maximum it can be.
    .   @param confidence Confidence level, between 0 and 1, for the estimated transformation. Anything
    .   between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation
    .   significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation.
    .   @param refineIters Maximum number of iterations of refining algorithm (Levenberg-Marquardt).
    .   Passing 0 will disable refining, so the output matrix will be output of robust method.
    .   
    .   @return Output 2D affine transformation (4 degrees of freedom) matrix \f$2 \times 3\f$ or
    .   empty matrix if transformation could not be estimated.
    .   
    .   The function estimates an optimal 2D affine transformation with 4 degrees of freedom limited to
    .   combinations of translation, rotation, and uniform scaling. Uses the selected algorithm for robust
    .   estimation.
    .   
    .   The computed transformation is then refined further (using only inliers) with the
    .   Levenberg-Marquardt method to reduce the re-projection error even more.
    .   
    .   Estimated transformation matrix is:
    .   \f[ \begin{bmatrix} \cos(\theta)s & -\sin(\theta)s & tx \\
    .   \sin(\theta)s & \cos(\theta)s & ty
    .   \end{bmatrix} \f]
    .   Where \f$ \theta \f$ is the rotation angle, \f$ s \f$ the scaling factor and \f$ tx, ty \f$ are
    .   translations in \f$ x, y \f$ axes respectively.
    .   
    .   @note
    .   The RANSAC method can handle practically any ratio of outliers but need a threshold to
    .   distinguish inliers from outliers. The method LMeDS does not need any threshold but it works
    .   correctly only when there are more than 50% of inliers.
    .   
    .   @sa estimateAffine2D, getAffineTransform
    """
    pass

def estimateRigidTransform(src, dst, fullAffine): # real signature unknown; restored from __doc__
    """
    estimateRigidTransform(src, dst, fullAffine) -> retval
    .   @brief Computes an optimal affine transformation between two 2D point sets.
    .   
    .   @param src First input 2D point set stored in std::vector or Mat, or an image stored in Mat.
    .   @param dst Second input 2D point set of the same size and the same type as A, or another image.
    .   @param fullAffine If true, the function finds an optimal affine transformation with no additional
    .   restrictions (6 degrees of freedom). Otherwise, the class of transformations to choose from is
    .   limited to combinations of translation, rotation, and uniform scaling (4 degrees of freedom).
    .   
    .   The function finds an optimal affine transform *[A|b]* (a 2 x 3 floating-point matrix) that
    .   approximates best the affine transformation between:
    .   
    .   *   Two point sets
    .   *   Two raster images. In this case, the function first finds some features in the src image and
    .   finds the corresponding features in dst image. After that, the problem is reduced to the first
    .   case.
    .   In case of point sets, the problem is formulated as follows: you need to find a 2x2 matrix *A* and
    .   2x1 vector *b* so that:
    .   
    .   \f[[A^*|b^*] = arg  \min _{[A|b]}  \sum _i  \| \texttt{dst}[i] - A { \texttt{src}[i]}^T - b  \| ^2\f]
    .   where src[i] and dst[i] are the i-th points in src and dst, respectively
    .   \f$[A|b]\f$ can be either arbitrary (when fullAffine=true ) or have a form of
    .   \f[\begin{bmatrix} a_{11} & a_{12} & b_1  \\ -a_{12} & a_{11} & b_2  \end{bmatrix}\f]
    .   when fullAffine=false.
    .   
    .   @sa
    .   estimateAffine2D, estimateAffinePartial2D, getAffineTransform, getPerspectiveTransform, findHomography
    """
    pass

def exp(src, dst=None): # real signature unknown; restored from __doc__
    """
    exp(src[, dst]) -> dst
    .   @brief Calculates the exponent of every array element.
    .   
    .   The function cv::exp calculates the exponent of every element of the input
    .   array:
    .   \f[\texttt{dst} [I] = e^{ src(I) }\f]
    .   
    .   The maximum relative error is about 7e-6 for single-precision input and
    .   less than 1e-10 for double-precision input. Currently, the function
    .   converts denormalized values to zeros on output. Special values (NaN,
    .   Inf) are not handled.
    .   @param src input array.
    .   @param dst output array of the same size and type as src.
    .   @sa log , cartToPolar , polarToCart , phase , pow , sqrt , magnitude
    """
    pass

def extractChannel(src, coi, dst=None): # real signature unknown; restored from __doc__
    """
    extractChannel(src, coi[, dst]) -> dst
    .   @brief Extracts a single channel from src (coi is 0-based index)
    .   @param src input array
    .   @param dst output array
    .   @param coi index of channel to extract
    .   @sa mixChannels, split
    """
    pass

def FarnebackOpticalFlow_create(numLevels=None, pyrScale=None, fastPyramids=None, winSize=None, numIters=None, polyN=None, polySigma=None, flags=None): # real signature unknown; restored from __doc__
    """
    FarnebackOpticalFlow_create([, numLevels[, pyrScale[, fastPyramids[, winSize[, numIters[, polyN[, polySigma[, flags]]]]]]]]) -> retval
    .
    """
    pass

def fastAtan2(y, x): # real signature unknown; restored from __doc__
    """
    fastAtan2(y, x) -> retval
    .   @brief Calculates the angle of a 2D vector in degrees.
    .   
    .   The function fastAtan2 calculates the full-range angle of an input 2D vector. The angle is measured
    .   in degrees and varies from 0 to 360 degrees. The accuracy is about 0.3 degrees.
    .   @param x x-coordinate of the vector.
    .   @param y y-coordinate of the vector.
    """
    pass

def FastFeatureDetector_create(threshold=None, nonmaxSuppression=None, type=None): # real signature unknown; restored from __doc__
    """
    FastFeatureDetector_create([, threshold[, nonmaxSuppression[, type]]]) -> retval
    .
    """
    pass

def fastNlMeansDenoising(src, dst=None, h=None, templateWindowSize=None, searchWindowSize=None): # real signature unknown; restored from __doc__
    """
    fastNlMeansDenoising(src[, dst[, h[, templateWindowSize[, searchWindowSize]]]]) -> dst
    .   @brief Perform image denoising using Non-local Means Denoising algorithm
    .   <http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/> with several computational
    .   optimizations. Noise expected to be a gaussian white noise
    .   
    .   @param src Input 8-bit 1-channel, 2-channel, 3-channel or 4-channel image.
    .   @param dst Output image with the same size and type as src .
    .   @param templateWindowSize Size in pixels of the template patch that is used to compute weights.
    .   Should be odd. Recommended value 7 pixels
    .   @param searchWindowSize Size in pixels of the window that is used to compute weighted average for
    .   given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater
    .   denoising time. Recommended value 21 pixels
    .   @param h Parameter regulating filter strength. Big h value perfectly removes noise but also
    .   removes image details, smaller h value preserves details but also preserves some noise
    .   
    .   This function expected to be applied to grayscale images. For colored images look at
    .   fastNlMeansDenoisingColored. Advanced usage of this functions can be manual denoising of colored
    .   image in different colorspaces. Such approach is used in fastNlMeansDenoisingColored by converting
    .   image to CIELAB colorspace and then separately denoise L and AB components with different h
    .   parameter.
    
    
    
    fastNlMeansDenoising(src, h[, dst[, templateWindowSize[, searchWindowSize[, normType]]]]) -> dst
    .   @brief Perform image denoising using Non-local Means Denoising algorithm
    .   <http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/> with several computational
    .   optimizations. Noise expected to be a gaussian white noise
    .   
    .   @param src Input 8-bit or 16-bit (only with NORM_L1) 1-channel,
    .   2-channel, 3-channel or 4-channel image.
    .   @param dst Output image with the same size and type as src .
    .   @param templateWindowSize Size in pixels of the template patch that is used to compute weights.
    .   Should be odd. Recommended value 7 pixels
    .   @param searchWindowSize Size in pixels of the window that is used to compute weighted average for
    .   given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater
    .   denoising time. Recommended value 21 pixels
    .   @param h Array of parameters regulating filter strength, either one
    .   parameter applied to all channels or one per channel in dst. Big h value
    .   perfectly removes noise but also removes image details, smaller h
    .   value preserves details but also preserves some noise
    .   @param normType Type of norm used for weight calculation. Can be either NORM_L2 or NORM_L1
    .   
    .   This function expected to be applied to grayscale images. For colored images look at
    .   fastNlMeansDenoisingColored. Advanced usage of this functions can be manual denoising of colored
    .   image in different colorspaces. Such approach is used in fastNlMeansDenoisingColored by converting
    .   image to CIELAB colorspace and then separately denoise L and AB components with different h
    .   parameter.
    """
    pass

def fastNlMeansDenoisingColored(src, dst=None, h=None, hColor=None, templateWindowSize=None, searchWindowSize=None): # real signature unknown; restored from __doc__
    """
    fastNlMeansDenoisingColored(src[, dst[, h[, hColor[, templateWindowSize[, searchWindowSize]]]]]) -> dst
    .   @brief Modification of fastNlMeansDenoising function for colored images
    .   
    .   @param src Input 8-bit 3-channel image.
    .   @param dst Output image with the same size and type as src .
    .   @param templateWindowSize Size in pixels of the template patch that is used to compute weights.
    .   Should be odd. Recommended value 7 pixels
    .   @param searchWindowSize Size in pixels of the window that is used to compute weighted average for
    .   given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater
    .   denoising time. Recommended value 21 pixels
    .   @param h Parameter regulating filter strength for luminance component. Bigger h value perfectly
    .   removes noise but also removes image details, smaller h value preserves details but also preserves
    .   some noise
    .   @param hColor The same as h but for color components. For most images value equals 10
    .   will be enough to remove colored noise and do not distort colors
    .   
    .   The function converts image to CIELAB colorspace and then separately denoise L and AB components
    .   with given h parameters using fastNlMeansDenoising function.
    """
    pass

def fastNlMeansDenoisingColoredMulti(srcImgs, imgToDenoiseIndex, temporalWindowSize, dst=None, h=None, hColor=None, templateWindowSize=None, searchWindowSize=None): # real signature unknown; restored from __doc__
    """
    fastNlMeansDenoisingColoredMulti(srcImgs, imgToDenoiseIndex, temporalWindowSize[, dst[, h[, hColor[, templateWindowSize[, searchWindowSize]]]]]) -> dst
    .   @brief Modification of fastNlMeansDenoisingMulti function for colored images sequences
    .   
    .   @param srcImgs Input 8-bit 3-channel images sequence. All images should have the same type and
    .   size.
    .   @param imgToDenoiseIndex Target image to denoise index in srcImgs sequence
    .   @param temporalWindowSize Number of surrounding images to use for target image denoising. Should
    .   be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to
    .   imgToDenoiseIndex - temporalWindowSize / 2 from srcImgs will be used to denoise
    .   srcImgs[imgToDenoiseIndex] image.
    .   @param dst Output image with the same size and type as srcImgs images.
    .   @param templateWindowSize Size in pixels of the template patch that is used to compute weights.
    .   Should be odd. Recommended value 7 pixels
    .   @param searchWindowSize Size in pixels of the window that is used to compute weighted average for
    .   given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater
    .   denoising time. Recommended value 21 pixels
    .   @param h Parameter regulating filter strength for luminance component. Bigger h value perfectly
    .   removes noise but also removes image details, smaller h value preserves details but also preserves
    .   some noise.
    .   @param hColor The same as h but for color components.
    .   
    .   The function converts images to CIELAB colorspace and then separately denoise L and AB components
    .   with given h parameters using fastNlMeansDenoisingMulti function.
    """
    pass

def fastNlMeansDenoisingMulti(srcImgs, imgToDenoiseIndex, temporalWindowSize, dst=None, h=None, templateWindowSize=None, searchWindowSize=None): # real signature unknown; restored from __doc__
    """
    fastNlMeansDenoisingMulti(srcImgs, imgToDenoiseIndex, temporalWindowSize[, dst[, h[, templateWindowSize[, searchWindowSize]]]]) -> dst
    .   @brief Modification of fastNlMeansDenoising function for images sequence where consequtive images have been
    .   captured in small period of time. For example video. This version of the function is for grayscale
    .   images or for manual manipulation with colorspaces. For more details see
    .   <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.131.6394>
    .   
    .   @param srcImgs Input 8-bit 1-channel, 2-channel, 3-channel or
    .   4-channel images sequence. All images should have the same type and
    .   size.
    .   @param imgToDenoiseIndex Target image to denoise index in srcImgs sequence
    .   @param temporalWindowSize Number of surrounding images to use for target image denoising. Should
    .   be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to
    .   imgToDenoiseIndex - temporalWindowSize / 2 from srcImgs will be used to denoise
    .   srcImgs[imgToDenoiseIndex] image.
    .   @param dst Output image with the same size and type as srcImgs images.
    .   @param templateWindowSize Size in pixels of the template patch that is used to compute weights.
    .   Should be odd. Recommended value 7 pixels
    .   @param searchWindowSize Size in pixels of the window that is used to compute weighted average for
    .   given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater
    .   denoising time. Recommended value 21 pixels
    .   @param h Parameter regulating filter strength. Bigger h value
    .   perfectly removes noise but also removes image details, smaller h
    .   value preserves details but also preserves some noise
    
    
    
    fastNlMeansDenoisingMulti(srcImgs, imgToDenoiseIndex, temporalWindowSize, h[, dst[, templateWindowSize[, searchWindowSize[, normType]]]]) -> dst
    .   @brief Modification of fastNlMeansDenoising function for images sequence where consequtive images have been
    .   captured in small period of time. For example video. This version of the function is for grayscale
    .   images or for manual manipulation with colorspaces. For more details see
    .   <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.131.6394>
    .   
    .   @param srcImgs Input 8-bit or 16-bit (only with NORM_L1) 1-channel,
    .   2-channel, 3-channel or 4-channel images sequence. All images should
    .   have the same type and size.
    .   @param imgToDenoiseIndex Target image to denoise index in srcImgs sequence
    .   @param temporalWindowSize Number of surrounding images to use for target image denoising. Should
    .   be odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to
    .   imgToDenoiseIndex - temporalWindowSize / 2 from srcImgs will be used to denoise
    .   srcImgs[imgToDenoiseIndex] image.
    .   @param dst Output image with the same size and type as srcImgs images.
    .   @param templateWindowSize Size in pixels of the template patch that is used to compute weights.
    .   Should be odd. Recommended value 7 pixels
    .   @param searchWindowSize Size in pixels of the window that is used to compute weighted average for
    .   given pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater
    .   denoising time. Recommended value 21 pixels
    .   @param h Array of parameters regulating filter strength, either one
    .   parameter applied to all channels or one per channel in dst. Big h value
    .   perfectly removes noise but also removes image details, smaller h
    .   value preserves details but also preserves some noise
    .   @param normType Type of norm used for weight calculation. Can be either NORM_L2 or NORM_L1
    """
    pass

def FileNode(): # real signature unknown; restored from __doc__
    """
    FileNode() -> <FileNode object>
    .   @brief The constructors.
    .   
    .   These constructors are used to create a default file node, construct it from obsolete structures or
    .   from the another file node.
    """
    pass

def FileStorage(source=None, flags=None, encoding=None): # real signature unknown; restored from __doc__
    """
    FileStorage([source, flags[, encoding]]) -> <FileStorage object>
    .   @brief The constructors.
    .   
    .   The full constructor opens the file. Alternatively you can use the default constructor and then
    .   call FileStorage::open.
    """
    pass

def fillConvexPoly(img, points, color, lineType=None, shift=None): # real signature unknown; restored from __doc__
    """
    fillConvexPoly(img, points, color[, lineType[, shift]]) -> img
    .   @brief Fills a convex polygon.
    .   
    .   The function fillConvexPoly draws a filled convex polygon. This function is much faster than the
    .   function cv::fillPoly . It can fill not only convex polygons but any monotonic polygon without
    .   self-intersections, that is, a polygon whose contour intersects every horizontal line (scan line)
    .   twice at the most (though, its top-most and/or the bottom edge could be horizontal).
    .   
    .   @param img Image.
    .   @param points Polygon vertices.
    .   @param color Polygon color.
    .   @param lineType Type of the polygon boundaries. See the line description.
    .   @param shift Number of fractional bits in the vertex coordinates.
    """
    pass

def fillPoly(img, pts, color, lineType=None, shift=None, offset=None): # real signature unknown; restored from __doc__
    """
    fillPoly(img, pts, color[, lineType[, shift[, offset]]]) -> img
    .   @brief Fills the area bounded by one or more polygons.
    .   
    .   The function fillPoly fills an area bounded by several polygonal contours. The function can fill
    .   complex areas, for example, areas with holes, contours with self-intersections (some of their
    .   parts), and so forth.
    .   
    .   @param img Image.
    .   @param pts Array of polygons where each polygon is represented as an array of points.
    .   @param color Polygon color.
    .   @param lineType Type of the polygon boundaries. See the line description.
    .   @param shift Number of fractional bits in the vertex coordinates.
    .   @param offset Optional offset of all points of the contours.
    """
    pass

def filter2D(src, ddepth, kernel, dst=None, anchor=None, delta=None, borderType=None): # real signature unknown; restored from __doc__
    """
    filter2D(src, ddepth, kernel[, dst[, anchor[, delta[, borderType]]]]) -> dst
    .   @brief Convolves an image with the kernel.
    .   
    .   The function applies an arbitrary linear filter to an image. In-place operation is supported. When
    .   the aperture is partially outside the image, the function interpolates outlier pixel values
    .   according to the specified border mode.
    .   
    .   The function does actually compute correlation, not the convolution:
    .   
    .   \f[\texttt{dst} (x,y) =  \sum _{ \stackrel{0\leq x' < \texttt{kernel.cols},}{0\leq y' < \texttt{kernel.rows}} }  \texttt{kernel} (x',y')* \texttt{src} (x+x'- \texttt{anchor.x} ,y+y'- \texttt{anchor.y} )\f]
    .   
    .   That is, the kernel is not mirrored around the anchor point. If you need a real convolution, flip
    .   the kernel using cv::flip and set the new anchor to `(kernel.cols - anchor.x - 1, kernel.rows -
    .   anchor.y - 1)`.
    .   
    .   The function uses the DFT-based algorithm in case of sufficiently large kernels (~`11 x 11` or
    .   larger) and the direct algorithm for small kernels.
    .   
    .   @param src input image.
    .   @param dst output image of the same size and the same number of channels as src.
    .   @param ddepth desired depth of the destination image, see @ref filter_depths "combinations"
    .   @param kernel convolution kernel (or rather a correlation kernel), a single-channel floating point
    .   matrix; if you want to apply different kernels to different channels, split the image into
    .   separate color planes using split and process them individually.
    .   @param anchor anchor of the kernel that indicates the relative position of a filtered point within
    .   the kernel; the anchor should lie within the kernel; default value (-1,-1) means that the anchor
    .   is at the kernel center.
    .   @param delta optional value added to the filtered pixels before storing them in dst.
    .   @param borderType pixel extrapolation method, see cv::BorderTypes
    .   @sa  sepFilter2D, dft, matchTemplate
    """
    pass

def filterSpeckles(img, newVal, maxSpeckleSize, maxDiff, buf=None): # real signature unknown; restored from __doc__
    """
    filterSpeckles(img, newVal, maxSpeckleSize, maxDiff[, buf]) -> img, buf
    .   @brief Filters off small noise blobs (speckles) in the disparity map
    .   
    .   @param img The input 16-bit signed disparity image
    .   @param newVal The disparity value used to paint-off the speckles
    .   @param maxSpeckleSize The maximum speckle size to consider it a speckle. Larger blobs are not
    .   affected by the algorithm
    .   @param maxDiff Maximum difference between neighbor disparity pixels to put them into the same
    .   blob. Note that since StereoBM, StereoSGBM and may be other algorithms return a fixed-point
    .   disparity map, where disparity values are multiplied by 16, this scale factor should be taken into
    .   account when specifying this parameter value.
    .   @param buf The optional temporary buffer to avoid memory allocation within the function.
    """
    pass

def findChessboardCorners(image, patternSize, corners=None, flags=None): # real signature unknown; restored from __doc__
    """
    findChessboardCorners(image, patternSize[, corners[, flags]]) -> retval, corners
    .   @brief Finds the positions of internal corners of the chessboard.
    .   
    .   @param image Source chessboard view. It must be an 8-bit grayscale or color image.
    .   @param patternSize Number of inner corners per a chessboard row and column
    .   ( patternSize = cvSize(points_per_row,points_per_colum) = cvSize(columns,rows) ).
    .   @param corners Output array of detected corners.
    .   @param flags Various operation flags that can be zero or a combination of the following values:
    .   -   **CALIB_CB_ADAPTIVE_THRESH** Use adaptive thresholding to convert the image to black
    .   and white, rather than a fixed threshold level (computed from the average image brightness).
    .   -   **CALIB_CB_NORMALIZE_IMAGE** Normalize the image gamma with equalizeHist before
    .   applying fixed or adaptive thresholding.
    .   -   **CALIB_CB_FILTER_QUADS** Use additional criteria (like contour area, perimeter,
    .   square-like shape) to filter out false quads extracted at the contour retrieval stage.
    .   -   **CALIB_CB_FAST_CHECK** Run a fast check on the image that looks for chessboard corners,
    .   and shortcut the call if none is found. This can drastically speed up the call in the
    .   degenerate condition when no chessboard is observed.
    .   
    .   The function attempts to determine whether the input image is a view of the chessboard pattern and
    .   locate the internal chessboard corners. The function returns a non-zero value if all of the corners
    .   are found and they are placed in a certain order (row by row, left to right in every row).
    .   Otherwise, if the function fails to find all the corners or reorder them, it returns 0. For example,
    .   a regular chessboard has 8 x 8 squares and 7 x 7 internal corners, that is, points where the black
    .   squares touch each other. The detected coordinates are approximate, and to determine their positions
    .   more accurately, the function calls cornerSubPix. You also may use the function cornerSubPix with
    .   different parameters if returned coordinates are not accurate enough.
    .   
    .   Sample usage of detecting and drawing chessboard corners: :
    .   @code
    .   Size patternsize(8,6); //interior number of corners
    .   Mat gray = ....; //source image
    .   vector<Point2f> corners; //this will be filled by the detected corners
    .   
    .   //CALIB_CB_FAST_CHECK saves a lot of time on images
    .   //that do not contain any chessboard corners
    .   bool patternfound = findChessboardCorners(gray, patternsize, corners,
    .   CALIB_CB_ADAPTIVE_THRESH + CALIB_CB_NORMALIZE_IMAGE
    .   + CALIB_CB_FAST_CHECK);
    .   
    .   if(patternfound)
    .   cornerSubPix(gray, corners, Size(11, 11), Size(-1, -1),
    .   TermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 30, 0.1));
    .   
    .   drawChessboardCorners(img, patternsize, Mat(corners), patternfound);
    .   @endcode
    .   @note The function requires white space (like a square-thick border, the wider the better) around
    .   the board to make the detection more robust in various environments. Otherwise, if there is no
    .   border and the background is dark, the outer black squares cannot be segmented properly and so the
    .   square grouping and ordering algorithm fails.
    """
    pass

def findCirclesGrid(image, patternSize, flags, blobDetector, parameters, centers=None): # real signature unknown; restored from __doc__
    """
    findCirclesGrid(image, patternSize, flags, blobDetector, parameters[, centers]) -> retval, centers
    .   @brief Finds centers in the grid of circles.
    .   
    .   @param image grid view of input circles; it must be an 8-bit grayscale or color image.
    .   @param patternSize number of circles per row and column
    .   ( patternSize = Size(points_per_row, points_per_colum) ).
    .   @param centers output array of detected centers.
    .   @param flags various operation flags that can be one of the following values:
    .   -   **CALIB_CB_SYMMETRIC_GRID** uses symmetric pattern of circles.
    .   -   **CALIB_CB_ASYMMETRIC_GRID** uses asymmetric pattern of circles.
    .   -   **CALIB_CB_CLUSTERING** uses a special algorithm for grid detection. It is more robust to
    .   perspective distortions but much more sensitive to background clutter.
    .   @param blobDetector feature detector that finds blobs like dark circles on light background.
    .   @param parameters struct for finding circles in a grid pattern.
    .   
    .   The function attempts to determine whether the input image contains a grid of circles. If it is, the
    .   function locates centers of the circles. The function returns a non-zero value if all of the centers
    .   have been found and they have been placed in a certain order (row by row, left to right in every
    .   row). Otherwise, if the function fails to find all the corners or reorder them, it returns 0.
    .   
    .   Sample usage of detecting and drawing the centers of circles: :
    .   @code
    .   Size patternsize(7,7); //number of centers
    .   Mat gray = ....; //source image
    .   vector<Point2f> centers; //this will be filled by the detected centers
    .   
    .   bool patternfound = findCirclesGrid(gray, patternsize, centers);
    .   
    .   drawChessboardCorners(img, patternsize, Mat(centers), patternfound);
    .   @endcode
    .   @note The function requires white space (like a square-thick border, the wider the better) around
    .   the board to make the detection more robust in various environments.
    
    
    
    findCirclesGrid(image, patternSize[, centers[, flags[, blobDetector]]]) -> retval, centers
    .   @overload
    """
    pass

def findContours(image, mode, method, contours=None, hierarchy=None, offset=None): # real signature unknown; restored from __doc__
    """
    findContours(image, mode, method[, contours[, hierarchy[, offset]]]) -> image, contours, hierarchy
    .   @brief Finds contours in a binary image.
    .   
    .   The function retrieves contours from the binary image using the algorithm @cite Suzuki85 . The contours
    .   are a useful tool for shape analysis and object detection and recognition. See squares.cpp in the
    .   OpenCV sample directory.
    .   @note Since opencv 3.2 source image is not modified by this function.
    .   
    .   @param image Source, an 8-bit single-channel image. Non-zero pixels are treated as 1's. Zero
    .   pixels remain 0's, so the image is treated as binary . You can use cv::compare, cv::inRange, cv::threshold ,
    .   cv::adaptiveThreshold, cv::Canny, and others to create a binary image out of a grayscale or color one.
    .   If mode equals to cv::RETR_CCOMP or cv::RETR_FLOODFILL, the input can also be a 32-bit integer image of labels (CV_32SC1).
    .   @param contours Detected contours. Each contour is stored as a vector of points (e.g.
    .   std::vector<std::vector<cv::Point> >).
    .   @param hierarchy Optional output vector (e.g. std::vector<cv::Vec4i>), containing information about the image topology. It has
    .   as many elements as the number of contours. For each i-th contour contours[i], the elements
    .   hierarchy[i][0] , hierarchy[i][1] , hierarchy[i][2] , and hierarchy[i][3] are set to 0-based indices
    .   in contours of the next and previous contours at the same hierarchical level, the first child
    .   contour and the parent contour, respectively. If for the contour i there are no next, previous,
    .   parent, or nested contours, the corresponding elements of hierarchy[i] will be negative.
    .   @param mode Contour retrieval mode, see cv::RetrievalModes
    .   @param method Contour approximation method, see cv::ContourApproximationModes
    .   @param offset Optional offset by which every contour point is shifted. This is useful if the
    .   contours are extracted from the image ROI and then they should be analyzed in the whole image
    .   context.
    """
    pass

def findEssentialMat(points1, points2, cameraMatrix, method=None, prob=None, threshold=None, mask=None): # real signature unknown; restored from __doc__
    """
    findEssentialMat(points1, points2, cameraMatrix[, method[, prob[, threshold[, mask]]]]) -> retval, mask
    .   @brief Calculates an essential matrix from the corresponding points in two images.
    .   
    .   @param points1 Array of N (N \>= 5) 2D points from the first image. The point coordinates should
    .   be floating-point (single or double precision).
    .   @param points2 Array of the second image points of the same size and format as points1 .
    .   @param cameraMatrix Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ .
    .   Note that this function assumes that points1 and points2 are feature points from cameras with the
    .   same camera matrix.
    .   @param method Method for computing a fundamental matrix.
    .   -   **RANSAC** for the RANSAC algorithm.
    .   -   **MEDS** for the LMedS algorithm.
    .   @param prob Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of
    .   confidence (probability) that the estimated matrix is correct.
    .   @param threshold Parameter used for RANSAC. It is the maximum distance from a point to an epipolar
    .   line in pixels, beyond which the point is considered an outlier and is not used for computing the
    .   final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the
    .   point localization, image resolution, and the image noise.
    .   @param mask Output array of N elements, every element of which is set to 0 for outliers and to 1
    .   for the other points. The array is computed only in the RANSAC and LMedS methods.
    .   
    .   This function estimates essential matrix based on the five-point algorithm solver in @cite Nister03 .
    .   @cite SteweniusCFS is also a related. The epipolar geometry is described by the following equation:
    .   
    .   \f[[p_2; 1]^T K^{-T} E K^{-1} [p_1; 1] = 0\f]
    .   
    .   where \f$E\f$ is an essential matrix, \f$p_1\f$ and \f$p_2\f$ are corresponding points in the first and the
    .   second images, respectively. The result of this function may be passed further to
    .   decomposeEssentialMat or recoverPose to recover the relative pose between cameras.
    
    
    
    findEssentialMat(points1, points2[, focal[, pp[, method[, prob[, threshold[, mask]]]]]]) -> retval, mask
    .   @overload
    .   @param points1 Array of N (N \>= 5) 2D points from the first image. The point coordinates should
    .   be floating-point (single or double precision).
    .   @param points2 Array of the second image points of the same size and format as points1 .
    .   @param focal focal length of the camera. Note that this function assumes that points1 and points2
    .   are feature points from cameras with same focal length and principal point.
    .   @param pp principal point of the camera.
    .   @param method Method for computing a fundamental matrix.
    .   -   **RANSAC** for the RANSAC algorithm.
    .   -   **LMEDS** for the LMedS algorithm.
    .   @param threshold Parameter used for RANSAC. It is the maximum distance from a point to an epipolar
    .   line in pixels, beyond which the point is considered an outlier and is not used for computing the
    .   final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the
    .   point localization, image resolution, and the image noise.
    .   @param prob Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level of
    .   confidence (probability) that the estimated matrix is correct.
    .   @param mask Output array of N elements, every element of which is set to 0 for outliers and to 1
    .   for the other points. The array is computed only in the RANSAC and LMedS methods.
    .   
    .   This function differs from the one above that it computes camera matrix from focal length and
    .   principal point:
    .   
    .   \f[K =
    .   \begin{bmatrix}
    .   f & 0 & x_{pp}  \\
    .   0 & f & y_{pp}  \\
    .   0 & 0 & 1
    .   \end{bmatrix}\f]
    """
    pass

def findFundamentalMat(points1, points2, method=None, param1=None, param2=None, mask=None): # real signature unknown; restored from __doc__
    """
    findFundamentalMat(points1, points2[, method[, param1[, param2[, mask]]]]) -> retval, mask
    .   @brief Calculates a fundamental matrix from the corresponding points in two images.
    .   
    .   @param points1 Array of N points from the first image. The point coordinates should be
    .   floating-point (single or double precision).
    .   @param points2 Array of the second image points of the same size and format as points1 .
    .   @param method Method for computing a fundamental matrix.
    .   -   **CV_FM_7POINT** for a 7-point algorithm. \f$N = 7\f$
    .   -   **CV_FM_8POINT** for an 8-point algorithm. \f$N \ge 8\f$
    .   -   **CV_FM_RANSAC** for the RANSAC algorithm. \f$N \ge 8\f$
    .   -   **CV_FM_LMEDS** for the LMedS algorithm. \f$N \ge 8\f$
    .   @param param1 Parameter used for RANSAC. It is the maximum distance from a point to an epipolar
    .   line in pixels, beyond which the point is considered an outlier and is not used for computing the
    .   final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the
    .   point localization, image resolution, and the image noise.
    .   @param param2 Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level
    .   of confidence (probability) that the estimated matrix is correct.
    .   @param mask
    .   
    .   The epipolar geometry is described by the following equation:
    .   
    .   \f[[p_2; 1]^T F [p_1; 1] = 0\f]
    .   
    .   where \f$F\f$ is a fundamental matrix, \f$p_1\f$ and \f$p_2\f$ are corresponding points in the first and the
    .   second images, respectively.
    .   
    .   The function calculates the fundamental matrix using one of four methods listed above and returns
    .   the found fundamental matrix. Normally just one matrix is found. But in case of the 7-point
    .   algorithm, the function may return up to 3 solutions ( \f$9 \times 3\f$ matrix that stores all 3
    .   matrices sequentially).
    .   
    .   The calculated fundamental matrix may be passed further to computeCorrespondEpilines that finds the
    .   epipolar lines corresponding to the specified points. It can also be passed to
    .   stereoRectifyUncalibrated to compute the rectification transformation. :
    .   @code
    .   // Example. Estimation of fundamental matrix using the RANSAC algorithm
    .   int point_count = 100;
    .   vector<Point2f> points1(point_count);
    .   vector<Point2f> points2(point_count);
    .   
    .   // initialize the points here ...
    .   for( int i = 0; i < point_count; i++ )
    .   {
    .   points1[i] = ...;
    .   points2[i] = ...;
    .   }
    .   
    .   Mat fundamental_matrix =
    .   findFundamentalMat(points1, points2, FM_RANSAC, 3, 0.99);
    .   @endcode
    """
    pass

def findHomography(srcPoints, dstPoints, method=None, ransacReprojThreshold=None, mask=None, maxIters=None, confidence=None): # real signature unknown; restored from __doc__
    """
    findHomography(srcPoints, dstPoints[, method[, ransacReprojThreshold[, mask[, maxIters[, confidence]]]]]) -> retval, mask
    .   @brief Finds a perspective transformation between two planes.
    .   
    .   @param srcPoints Coordinates of the points in the original plane, a matrix of the type CV_32FC2
    .   or vector\<Point2f\> .
    .   @param dstPoints Coordinates of the points in the target plane, a matrix of the type CV_32FC2 or
    .   a vector\<Point2f\> .
    .   @param method Method used to computed a homography matrix. The following methods are possible:
    .   -   **0** - a regular method using all the points
    .   -   **RANSAC** - RANSAC-based robust method
    .   -   **LMEDS** - Least-Median robust method
    .   -   **RHO**    - PROSAC-based robust method
    .   @param ransacReprojThreshold Maximum allowed reprojection error to treat a point pair as an inlier
    .   (used in the RANSAC and RHO methods only). That is, if
    .   \f[\| \texttt{dstPoints} _i -  \texttt{convertPointsHomogeneous} ( \texttt{H} * \texttt{srcPoints} _i) \|  >  \texttt{ransacReprojThreshold}\f]
    .   then the point \f$i\f$ is considered an outlier. If srcPoints and dstPoints are measured in pixels,
    .   it usually makes sense to set this parameter somewhere in the range of 1 to 10.
    .   @param mask Optional output mask set by a robust method ( RANSAC or LMEDS ). Note that the input
    .   mask values are ignored.
    .   @param maxIters The maximum number of RANSAC iterations, 2000 is the maximum it can be.
    .   @param confidence Confidence level, between 0 and 1.
    .   
    .   The function finds and returns the perspective transformation \f$H\f$ between the source and the
    .   destination planes:
    .   
    .   \f[s_i  \vecthree{x'_i}{y'_i}{1} \sim H  \vecthree{x_i}{y_i}{1}\f]
    .   
    .   so that the back-projection error
    .   
    .   \f[\sum _i \left ( x'_i- \frac{h_{11} x_i + h_{12} y_i + h_{13}}{h_{31} x_i + h_{32} y_i + h_{33}} \right )^2+ \left ( y'_i- \frac{h_{21} x_i + h_{22} y_i + h_{23}}{h_{31} x_i + h_{32} y_i + h_{33}} \right )^2\f]
    .   
    .   is minimized. If the parameter method is set to the default value 0, the function uses all the point
    .   pairs to compute an initial homography estimate with a simple least-squares scheme.
    .   
    .   However, if not all of the point pairs ( \f$srcPoints_i\f$, \f$dstPoints_i\f$ ) fit the rigid perspective
    .   transformation (that is, there are some outliers), this initial estimate will be poor. In this case,
    .   you can use one of the three robust methods. The methods RANSAC, LMeDS and RHO try many different
    .   random subsets of the corresponding point pairs (of four pairs each), estimate the homography matrix
    .   using this subset and a simple least-square algorithm, and then compute the quality/goodness of the
    .   computed homography (which is the number of inliers for RANSAC or the median re-projection error for
    .   LMeDs). The best subset is then used to produce the initial estimate of the homography matrix and
    .   the mask of inliers/outliers.
    .   
    .   Regardless of the method, robust or not, the computed homography matrix is refined further (using
    .   inliers only in case of a robust method) with the Levenberg-Marquardt method to reduce the
    .   re-projection error even more.
    .   
    .   The methods RANSAC and RHO can handle practically any ratio of outliers but need a threshold to
    .   distinguish inliers from outliers. The method LMeDS does not need any threshold but it works
    .   correctly only when there are more than 50% of inliers. Finally, if there are no outliers and the
    .   noise is rather small, use the default method (method=0).
    .   
    .   The function is used to find initial intrinsic and extrinsic matrices. Homography matrix is
    .   determined up to a scale. Thus, it is normalized so that \f$h_{33}=1\f$. Note that whenever an H matrix
    .   cannot be estimated, an empty one will be returned.
    .   
    .   @sa
    .   getAffineTransform, estimateAffine2D, estimateAffinePartial2D, getPerspectiveTransform, warpPerspective,
    .   perspectiveTransform
    .   
    .   
    .   @note
    .   -   A example on calculating a homography for image matching can be found at
    .   opencv_source_code/samples/cpp/video_homography.cpp
    """
    pass

def findNonZero(src, idx=None): # real signature unknown; restored from __doc__
    """
    findNonZero(src[, idx]) -> idx
    .   @brief Returns the list of locations of non-zero pixels
    .   
    .   Given a binary matrix (likely returned from an operation such
    .   as threshold(), compare(), >, ==, etc, return all of
    .   the non-zero indices as a cv::Mat or std::vector<cv::Point> (x,y)
    .   For example:
    .   @code{.cpp}
    .   cv::Mat binaryImage; // input, binary image
    .   cv::Mat locations;   // output, locations of non-zero pixels
    .   cv::findNonZero(binaryImage, locations);
    .   
    .   // access pixel coordinates
    .   Point pnt = locations.at<Point>(i);
    .   @endcode
    .   or
    .   @code{.cpp}
    .   cv::Mat binaryImage; // input, binary image
    .   vector<Point> locations;   // output, locations of non-zero pixels
    .   cv::findNonZero(binaryImage, locations);
    .   
    .   // access pixel coordinates
    .   Point pnt = locations[i];
    .   @endcode
    .   @param src single-channel array (type CV_8UC1)
    .   @param idx the output array, type of cv::Mat or std::vector<Point>, corresponding to non-zero indices in the input
    """
    pass

def findTransformECC(templateImage, inputImage, warpMatrix, motionType=None, criteria=None, inputMask=None): # real signature unknown; restored from __doc__
    """
    findTransformECC(templateImage, inputImage, warpMatrix[, motionType[, criteria[, inputMask]]]) -> retval, warpMatrix
    .   @brief Finds the geometric transform (warp) between two images in terms of the ECC criterion @cite EP08 .
    .   
    .   @param templateImage single-channel template image; CV_8U or CV_32F array.
    .   @param inputImage single-channel input image which should be warped with the final warpMatrix in
    .   order to provide an image similar to templateImage, same type as temlateImage.
    .   @param warpMatrix floating-point \f$2\times 3\f$ or \f$3\times 3\f$ mapping matrix (warp).
    .   @param motionType parameter, specifying the type of motion:
    .   -   **MOTION_TRANSLATION** sets a translational motion model; warpMatrix is \f$2\times 3\f$ with
    .   the first \f$2\times 2\f$ part being the unity matrix and the rest two parameters being
    .   estimated.
    .   -   **MOTION_EUCLIDEAN** sets a Euclidean (rigid) transformation as motion model; three
    .   parameters are estimated; warpMatrix is \f$2\times 3\f$.
    .   -   **MOTION_AFFINE** sets an affine motion model (DEFAULT); six parameters are estimated;
    .   warpMatrix is \f$2\times 3\f$.
    .   -   **MOTION_HOMOGRAPHY** sets a homography as a motion model; eight parameters are
    .   estimated;\`warpMatrix\` is \f$3\times 3\f$.
    .   @param criteria parameter, specifying the termination criteria of the ECC algorithm;
    .   criteria.epsilon defines the threshold of the increment in the correlation coefficient between two
    .   iterations (a negative criteria.epsilon makes criteria.maxcount the only termination criterion).
    .   Default values are shown in the declaration above.
    .   @param inputMask An optional mask to indicate valid values of inputImage.
    .   
    .   The function estimates the optimum transformation (warpMatrix) with respect to ECC criterion
    .   (@cite EP08), that is
    .   
    .   \f[\texttt{warpMatrix} = \texttt{warpMatrix} = \arg\max_{W} \texttt{ECC}(\texttt{templateImage}(x,y),\texttt{inputImage}(x',y'))\f]
    .   
    .   where
    .   
    .   \f[\begin{bmatrix} x' \\ y' \end{bmatrix} = W \cdot \begin{bmatrix} x \\ y \\ 1 \end{bmatrix}\f]
    .   
    .   (the equation holds with homogeneous coordinates for homography). It returns the final enhanced
    .   correlation coefficient, that is the correlation coefficient between the template image and the
    .   final warped input image. When a \f$3\times 3\f$ matrix is given with motionType =0, 1 or 2, the third
    .   row is ignored.
    .   
    .   Unlike findHomography and estimateRigidTransform, the function findTransformECC implements an
    .   area-based alignment that builds on intensity similarities. In essence, the function updates the
    .   initial transformation that roughly aligns the images. If this information is missing, the identity
    .   warp (unity matrix) is used as an initialization. Note that if images undergo strong
    .   displacements/rotations, an initial transformation that roughly aligns the images is necessary
    .   (e.g., a simple euclidean/similarity transform that allows for the images showing the same image
    .   content approximately). Use inverse warping in the second image to take an image close to the first
    .   one, i.e. use the flag WARP_INVERSE_MAP with warpAffine or warpPerspective. See also the OpenCV
    .   sample image_alignment.cpp that demonstrates the use of the function. Note that the function throws
    .   an exception if algorithm does not converges.
    .   
    .   @sa
    .   estimateAffine2D, estimateAffinePartial2D, findHomography
    """
    pass

def fitEllipse(points): # real signature unknown; restored from __doc__
    """
    fitEllipse(points) -> retval
    .   @brief Fits an ellipse around a set of 2D points.
    .   
    .   The function calculates the ellipse that fits (in a least-squares sense) a set of 2D points best of
    .   all. It returns the rotated rectangle in which the ellipse is inscribed. The first algorithm described by @cite Fitzgibbon95
    .   is used. Developer should keep in mind that it is possible that the returned
    .   ellipse/rotatedRect data contains negative indices, due to the data points being close to the
    .   border of the containing Mat element.
    .   
    .   @param points Input 2D point set, stored in std::vector\<\> or Mat
    """
    pass

def fitLine(points, distType, param, reps, aeps, line=None): # real signature unknown; restored from __doc__
    """
    fitLine(points, distType, param, reps, aeps[, line]) -> line
    .   @brief Fits a line to a 2D or 3D point set.
    .   
    .   The function fitLine fits a line to a 2D or 3D point set by minimizing \f$\sum_i \rho(r_i)\f$ where
    .   \f$r_i\f$ is a distance between the \f$i^{th}\f$ point, the line and \f$\rho(r)\f$ is a distance function, one
    .   of the following:
    .   -  DIST_L2
    .   \f[\rho (r) = r^2/2  \quad \text{(the simplest and the fastest least-squares method)}\f]
    .   - DIST_L1
    .   \f[\rho (r) = r\f]
    .   - DIST_L12
    .   \f[\rho (r) = 2  \cdot ( \sqrt{1 + \frac{r^2}{2}} - 1)\f]
    .   - DIST_FAIR
    .   \f[\rho \left (r \right ) = C^2  \cdot \left (  \frac{r}{C} -  \log{\left(1 + \frac{r}{C}\right)} \right )  \quad \text{where} \quad C=1.3998\f]
    .   - DIST_WELSCH
    .   \f[\rho \left (r \right ) =  \frac{C^2}{2} \cdot \left ( 1 -  \exp{\left(-\left(\frac{r}{C}\right)^2\right)} \right )  \quad \text{where} \quad C=2.9846\f]
    .   - DIST_HUBER
    .   \f[\rho (r) =  \fork{r^2/2}{if \(r < C\)}{C \cdot (r-C/2)}{otherwise} \quad \text{where} \quad C=1.345\f]
    .   
    .   The algorithm is based on the M-estimator ( <http://en.wikipedia.org/wiki/M-estimator> ) technique
    .   that iteratively fits the line using the weighted least-squares algorithm. After each iteration the
    .   weights \f$w_i\f$ are adjusted to be inversely proportional to \f$\rho(r_i)\f$ .
    .   
    .   @param points Input vector of 2D or 3D points, stored in std::vector\<\> or Mat.
    .   @param line Output line parameters. In case of 2D fitting, it should be a vector of 4 elements
    .   (like Vec4f) - (vx, vy, x0, y0), where (vx, vy) is a normalized vector collinear to the line and
    .   (x0, y0) is a point on the line. In case of 3D fitting, it should be a vector of 6 elements (like
    .   Vec6f) - (vx, vy, vz, x0, y0, z0), where (vx, vy, vz) is a normalized vector collinear to the line
    .   and (x0, y0, z0) is a point on the line.
    .   @param distType Distance used by the M-estimator, see cv::DistanceTypes
    .   @param param Numerical parameter ( C ) for some types of distances. If it is 0, an optimal value
    .   is chosen.
    .   @param reps Sufficient accuracy for the radius (distance between the coordinate origin and the line).
    .   @param aeps Sufficient accuracy for the angle. 0.01 would be a good default value for reps and aeps.
    """
    pass

def FlannBasedMatcher(indexParams=None, searchParams=None): # real signature unknown; restored from __doc__
    """
    FlannBasedMatcher([, indexParams[, searchParams]]) -> <FlannBasedMatcher object>
    .
    """
    pass

def FlannBasedMatcher_create(): # real signature unknown; restored from __doc__
    """
    FlannBasedMatcher_create() -> retval
    .
    """
    pass

def flip(src, flipCode, dst=None): # real signature unknown; restored from __doc__
    """
    flip(src, flipCode[, dst]) -> dst
    .   @brief Flips a 2D array around vertical, horizontal, or both axes.
    .   
    .   The function cv::flip flips the array in one of three different ways (row
    .   and column indices are 0-based):
    .   \f[\texttt{dst} _{ij} =
    .   \left\{
    .   \begin{array}{l l}
    .   \texttt{src} _{\texttt{src.rows}-i-1,j} & if\;  \texttt{flipCode} = 0 \\
    .   \texttt{src} _{i, \texttt{src.cols} -j-1} & if\;  \texttt{flipCode} > 0 \\
    .   \texttt{src} _{ \texttt{src.rows} -i-1, \texttt{src.cols} -j-1} & if\; \texttt{flipCode} < 0 \\
    .   \end{array}
    .   \right.\f]
    .   The example scenarios of using the function are the following:
    .   *   Vertical flipping of the image (flipCode == 0) to switch between
    .   top-left and bottom-left image origin. This is a typical operation
    .   in video processing on Microsoft Windows\* OS.
    .   *   Horizontal flipping of the image with the subsequent horizontal
    .   shift and absolute difference calculation to check for a
    .   vertical-axis symmetry (flipCode \> 0).
    .   *   Simultaneous horizontal and vertical flipping of the image with
    .   the subsequent shift and absolute difference calculation to check
    .   for a central symmetry (flipCode \< 0).
    .   *   Reversing the order of point arrays (flipCode \> 0 or
    .   flipCode == 0).
    .   @param src input array.
    .   @param dst output array of the same size and type as src.
    .   @param flipCode a flag to specify how to flip the array; 0 means
    .   flipping around the x-axis and positive value (for example, 1) means
    .   flipping around y-axis. Negative value (for example, -1) means flipping
    .   around both axes.
    .   @sa transpose , repeat , completeSymm
    """
    pass

def floodFill(image, mask, seedPoint, newVal, loDiff=None, upDiff=None, flags=None): # real signature unknown; restored from __doc__
    """
    floodFill(image, mask, seedPoint, newVal[, loDiff[, upDiff[, flags]]]) -> retval, image, mask, rect
    .   @brief Fills a connected component with the given color.
    .   
    .   The function cv::floodFill fills a connected component starting from the seed point with the specified
    .   color. The connectivity is determined by the color/brightness closeness of the neighbor pixels. The
    .   pixel at \f$(x,y)\f$ is considered to belong to the repainted domain if:
    .   
    .   - in case of a grayscale image and floating range
    .   \f[\texttt{src} (x',y')- \texttt{loDiff} \leq \texttt{src} (x,y)  \leq \texttt{src} (x',y')+ \texttt{upDiff}\f]
    .   
    .   
    .   - in case of a grayscale image and fixed range
    .   \f[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)- \texttt{loDiff} \leq \texttt{src} (x,y)  \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)+ \texttt{upDiff}\f]
    .   
    .   
    .   - in case of a color image and floating range
    .   \f[\texttt{src} (x',y')_r- \texttt{loDiff} _r \leq \texttt{src} (x,y)_r \leq \texttt{src} (x',y')_r+ \texttt{upDiff} _r,\f]
    .   \f[\texttt{src} (x',y')_g- \texttt{loDiff} _g \leq \texttt{src} (x,y)_g \leq \texttt{src} (x',y')_g+ \texttt{upDiff} _g\f]
    .   and
    .   \f[\texttt{src} (x',y')_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} (x',y')_b+ \texttt{upDiff} _b\f]
    .   
    .   
    .   - in case of a color image and fixed range
    .   \f[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_r- \texttt{loDiff} _r \leq \texttt{src} (x,y)_r \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_r+ \texttt{upDiff} _r,\f]
    .   \f[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_g- \texttt{loDiff} _g \leq \texttt{src} (x,y)_g \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_g+ \texttt{upDiff} _g\f]
    .   and
    .   \f[\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b+ \texttt{upDiff} _b\f]
    .   
    .   
    .   where \f$src(x',y')\f$ is the value of one of pixel neighbors that is already known to belong to the
    .   component. That is, to be added to the connected component, a color/brightness of the pixel should
    .   be close enough to:
    .   - Color/brightness of one of its neighbors that already belong to the connected component in case
    .   of a floating range.
    .   - Color/brightness of the seed point in case of a fixed range.
    .   
    .   Use these functions to either mark a connected component with the specified color in-place, or build
    .   a mask and then extract the contour, or copy the region to another image, and so on.
    .   
    .   @param image Input/output 1- or 3-channel, 8-bit, or floating-point image. It is modified by the
    .   function unless the FLOODFILL_MASK_ONLY flag is set in the second variant of the function. See
    .   the details below.
    .   @param mask Operation mask that should be a single-channel 8-bit image, 2 pixels wider and 2 pixels
    .   taller than image. Since this is both an input and output parameter, you must take responsibility
    .   of initializing it. Flood-filling cannot go across non-zero pixels in the input mask. For example,
    .   an edge detector output can be used as a mask to stop filling at edges. On output, pixels in the
    .   mask corresponding to filled pixels in the image are set to 1 or to the a value specified in flags
    .   as described below. It is therefore possible to use the same mask in multiple calls to the function
    .   to make sure the filled areas do not overlap.
    .   @param seedPoint Starting point.
    .   @param newVal New value of the repainted domain pixels.
    .   @param loDiff Maximal lower brightness/color difference between the currently observed pixel and
    .   one of its neighbors belonging to the component, or a seed pixel being added to the component.
    .   @param upDiff Maximal upper brightness/color difference between the currently observed pixel and
    .   one of its neighbors belonging to the component, or a seed pixel being added to the component.
    .   @param rect Optional output parameter set by the function to the minimum bounding rectangle of the
    .   repainted domain.
    .   @param flags Operation flags. The first 8 bits contain a connectivity value. The default value of
    .   4 means that only the four nearest neighbor pixels (those that share an edge) are considered. A
    .   connectivity value of 8 means that the eight nearest neighbor pixels (those that share a corner)
    .   will be considered. The next 8 bits (8-16) contain a value between 1 and 255 with which to fill
    .   the mask (the default value is 1). For example, 4 | ( 255 \<\< 8 ) will consider 4 nearest
    .   neighbours and fill the mask with a value of 255. The following additional options occupy higher
    .   bits and therefore may be further combined with the connectivity and mask fill values using
    .   bit-wise or (|), see cv::FloodFillFlags.
    .   
    .   @note Since the mask is larger than the filled image, a pixel \f$(x, y)\f$ in image corresponds to the
    .   pixel \f$(x+1, y+1)\f$ in the mask .
    .   
    .   @sa findContours
    """
    pass

def GaussianBlur(src, ksize, sigmaX, dst=None, sigmaY=None, borderType=None): # real signature unknown; restored from __doc__
    """
    GaussianBlur(src, ksize, sigmaX[, dst[, sigmaY[, borderType]]]) -> dst
    .   @brief Blurs an image using a Gaussian filter.
    .   
    .   The function convolves the source image with the specified Gaussian kernel. In-place filtering is
    .   supported.
    .   
    .   @param src input image; the image can have any number of channels, which are processed
    .   independently, but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
    .   @param dst output image of the same size and type as src.
    .   @param ksize Gaussian kernel size. ksize.width and ksize.height can differ but they both must be
    .   positive and odd. Or, they can be zero's and then they are computed from sigma.
    .   @param sigmaX Gaussian kernel standard deviation in X direction.
    .   @param sigmaY Gaussian kernel standard deviation in Y direction; if sigmaY is zero, it is set to be
    .   equal to sigmaX, if both sigmas are zeros, they are computed from ksize.width and ksize.height,
    .   respectively (see cv::getGaussianKernel for details); to fully control the result regardless of
    .   possible future modifications of all this semantics, it is recommended to specify all of ksize,
    .   sigmaX, and sigmaY.
    .   @param borderType pixel extrapolation method, see cv::BorderTypes
    .   
    .   @sa  sepFilter2D, filter2D, blur, boxFilter, bilateralFilter, medianBlur
    """
    pass

def gemm(src1, src2, alpha, src3, beta, dst=None, flags=None): # real signature unknown; restored from __doc__
    """
    gemm(src1, src2, alpha, src3, beta[, dst[, flags]]) -> dst
    .   @brief Performs generalized matrix multiplication.
    .   
    .   The function cv::gemm performs generalized matrix multiplication similar to the
    .   gemm functions in BLAS level 3. For example,
    .   `gemm(src1, src2, alpha, src3, beta, dst, GEMM_1_T + GEMM_3_T)`
    .   corresponds to
    .   \f[\texttt{dst} =  \texttt{alpha} \cdot \texttt{src1} ^T  \cdot \texttt{src2} +  \texttt{beta} \cdot \texttt{src3} ^T\f]
    .   
    .   In case of complex (two-channel) data, performed a complex matrix
    .   multiplication.
    .   
    .   The function can be replaced with a matrix expression. For example, the
    .   above call can be replaced with:
    .   @code{.cpp}
    .   dst = alpha*src1.t()*src2 + beta*src3.t();
    .   @endcode
    .   @param src1 first multiplied input matrix that could be real(CV_32FC1,
    .   CV_64FC1) or complex(CV_32FC2, CV_64FC2).
    .   @param src2 second multiplied input matrix of the same type as src1.
    .   @param alpha weight of the matrix product.
    .   @param src3 third optional delta matrix added to the matrix product; it
    .   should have the same type as src1 and src2.
    .   @param beta weight of src3.
    .   @param dst output matrix; it has the proper size and the same type as
    .   input matrices.
    .   @param flags operation flags (cv::GemmFlags)
    .   @sa mulTransposed , transform
    """
    pass

def getAffineTransform(src, dst): # real signature unknown; restored from __doc__
    """
    getAffineTransform(src, dst) -> retval
    .
    """
    pass

def getBuildInformation(): # real signature unknown; restored from __doc__
    """
    getBuildInformation() -> retval
    .   @brief Returns full configuration time cmake output.
    .   
    .   Returned value is raw cmake output including version control system revision, compiler version,
    .   compiler flags, enabled modules and third party libraries, etc. Output format depends on target
    .   architecture.
    """
    pass

def getCPUTickCount(): # real signature unknown; restored from __doc__
    """
    getCPUTickCount() -> retval
    .   @brief Returns the number of CPU ticks.
    .   
    .   The function returns the current number of CPU ticks on some architectures (such as x86, x64,
    .   PowerPC). On other platforms the function is equivalent to getTickCount. It can also be used for
    .   very accurate time measurements, as well as for RNG initialization. Note that in case of multi-CPU
    .   systems a thread, from which getCPUTickCount is called, can be suspended and resumed at another CPU
    .   with its own counter. So, theoretically (and practically) the subsequent calls to the function do
    .   not necessary return the monotonously increasing values. Also, since a modern CPU varies the CPU
    .   frequency depending on the load, the number of CPU clocks spent in some code cannot be directly
    .   converted to time units. Therefore, getTickCount is generally a preferable solution for measuring
    .   execution time.
    """
    pass

def getDefaultNewCameraMatrix(cameraMatrix, imgsize=None, centerPrincipalPoint=None): # real signature unknown; restored from __doc__
    """
    getDefaultNewCameraMatrix(cameraMatrix[, imgsize[, centerPrincipalPoint]]) -> retval
    .   @brief Returns the default new camera matrix.
    .   
    .   The function returns the camera matrix that is either an exact copy of the input cameraMatrix (when
    .   centerPrinicipalPoint=false ), or the modified one (when centerPrincipalPoint=true).
    .   
    .   In the latter case, the new camera matrix will be:
    .   
    .   \f[\begin{bmatrix} f_x && 0 && ( \texttt{imgSize.width} -1)*0.5  \\ 0 && f_y && ( \texttt{imgSize.height} -1)*0.5  \\ 0 && 0 && 1 \end{bmatrix} ,\f]
    .   
    .   where \f$f_x\f$ and \f$f_y\f$ are \f$(0,0)\f$ and \f$(1,1)\f$ elements of cameraMatrix, respectively.
    .   
    .   By default, the undistortion functions in OpenCV (see initUndistortRectifyMap, undistort) do not
    .   move the principal point. However, when you work with stereo, it is important to move the principal
    .   points in both views to the same y-coordinate (which is required by most of stereo correspondence
    .   algorithms), and may be to the same x-coordinate too. So, you can form the new camera matrix for
    .   each view where the principal points are located at the center.
    .   
    .   @param cameraMatrix Input camera matrix.
    .   @param imgsize Camera view image size in pixels.
    .   @param centerPrincipalPoint Location of the principal point in the new camera matrix. The
    .   parameter indicates whether this location should be at the image center or not.
    """
    pass

def getDerivKernels(dx, dy, ksize, kx=None, ky=None, normalize=None, ktype=None): # real signature unknown; restored from __doc__
    """
    getDerivKernels(dx, dy, ksize[, kx[, ky[, normalize[, ktype]]]]) -> kx, ky
    .   @brief Returns filter coefficients for computing spatial image derivatives.
    .   
    .   The function computes and returns the filter coefficients for spatial image derivatives. When
    .   `ksize=CV_SCHARR`, the Scharr \f$3 \times 3\f$ kernels are generated (see cv::Scharr). Otherwise, Sobel
    .   kernels are generated (see cv::Sobel). The filters are normally passed to sepFilter2D or to
    .   
    .   @param kx Output matrix of row filter coefficients. It has the type ktype .
    .   @param ky Output matrix of column filter coefficients. It has the type ktype .
    .   @param dx Derivative order in respect of x.
    .   @param dy Derivative order in respect of y.
    .   @param ksize Aperture size. It can be CV_SCHARR, 1, 3, 5, or 7.
    .   @param normalize Flag indicating whether to normalize (scale down) the filter coefficients or not.
    .   Theoretically, the coefficients should have the denominator \f$=2^{ksize*2-dx-dy-2}\f$. If you are
    .   going to filter floating-point images, you are likely to use the normalized kernels. But if you
    .   compute derivatives of an 8-bit image, store the results in a 16-bit image, and wish to preserve
    .   all the fractional bits, you may want to set normalize=false .
    .   @param ktype Type of filter coefficients. It can be CV_32f or CV_64F .
    """
    pass

def getGaborKernel(ksize, sigma, theta, lambd, gamma, psi=None, ktype=None): # real signature unknown; restored from __doc__
    """
    getGaborKernel(ksize, sigma, theta, lambd, gamma[, psi[, ktype]]) -> retval
    .   @brief Returns Gabor filter coefficients.
    .   
    .   For more details about gabor filter equations and parameters, see: [Gabor
    .   Filter](http://en.wikipedia.org/wiki/Gabor_filter).
    .   
    .   @param ksize Size of the filter returned.
    .   @param sigma Standard deviation of the gaussian envelope.
    .   @param theta Orientation of the normal to the parallel stripes of a Gabor function.
    .   @param lambd Wavelength of the sinusoidal factor.
    .   @param gamma Spatial aspect ratio.
    .   @param psi Phase offset.
    .   @param ktype Type of filter coefficients. It can be CV_32F or CV_64F .
    """
    pass

def getGaussianKernel(ksize, sigma, ktype=None): # real signature unknown; restored from __doc__
    """
    getGaussianKernel(ksize, sigma[, ktype]) -> retval
    .   @brief Returns Gaussian filter coefficients.
    .   
    .   The function computes and returns the \f$\texttt{ksize} \times 1\f$ matrix of Gaussian filter
    .   coefficients:
    .   
    .   \f[G_i= \alpha *e^{-(i-( \texttt{ksize} -1)/2)^2/(2* \texttt{sigma}^2)},\f]
    .   
    .   where \f$i=0..\texttt{ksize}-1\f$ and \f$\alpha\f$ is the scale factor chosen so that \f$\sum_i G_i=1\f$.
    .   
    .   Two of such generated kernels can be passed to sepFilter2D. Those functions automatically recognize
    .   smoothing kernels (a symmetrical kernel with sum of weights equal to 1) and handle them accordingly.
    .   You may also use the higher-level GaussianBlur.
    .   @param ksize Aperture size. It should be odd ( \f$\texttt{ksize} \mod 2 = 1\f$ ) and positive.
    .   @param sigma Gaussian standard deviation. If it is non-positive, it is computed from ksize as
    .   `sigma = 0.3*((ksize-1)*0.5 - 1) + 0.8`.
    .   @param ktype Type of filter coefficients. It can be CV_32F or CV_64F .
    .   @sa  sepFilter2D, getDerivKernels, getStructuringElement, GaussianBlur
    """
    pass

def getNumberOfCPUs(): # real signature unknown; restored from __doc__
    """
    getNumberOfCPUs() -> retval
    .   @brief Returns the number of logical CPUs available for the process.
    """
    pass

def getNumThreads(): # real signature unknown; restored from __doc__
    """
    getNumThreads() -> retval
    .   @brief Returns the number of threads used by OpenCV for parallel regions.
    .   
    .   Always returns 1 if OpenCV is built without threading support.
    .   
    .   The exact meaning of return value depends on the threading framework used by OpenCV library:
    .   - `TBB` - The number of threads, that OpenCV will try to use for parallel regions. If there is
    .   any tbb::thread_scheduler_init in user code conflicting with OpenCV, then function returns
    .   default number of threads used by TBB library.
    .   - `OpenMP` - An upper bound on the number of threads that could be used to form a new team.
    .   - `Concurrency` - The number of threads, that OpenCV will try to use for parallel regions.
    .   - `GCD` - Unsupported; returns the GCD thread pool limit (512) for compatibility.
    .   - `C=` - The number of threads, that OpenCV will try to use for parallel regions, if before
    .   called setNumThreads with threads \> 0, otherwise returns the number of logical CPUs,
    .   available for the process.
    .   @sa setNumThreads, getThreadNum
    """
    pass

def getOptimalDFTSize(vecsize): # real signature unknown; restored from __doc__
    """
    getOptimalDFTSize(vecsize) -> retval
    .   @brief Returns the optimal DFT size for a given vector size.
    .   
    .   DFT performance is not a monotonic function of a vector size. Therefore, when you calculate
    .   convolution of two arrays or perform the spectral analysis of an array, it usually makes sense to
    .   pad the input data with zeros to get a bit larger array that can be transformed much faster than the
    .   original one. Arrays whose size is a power-of-two (2, 4, 8, 16, 32, ...) are the fastest to process.
    .   Though, the arrays whose size is a product of 2's, 3's, and 5's (for example, 300 = 5\*5\*3\*2\*2)
    .   are also processed quite efficiently.
    .   
    .   The function cv::getOptimalDFTSize returns the minimum number N that is greater than or equal to vecsize
    .   so that the DFT of a vector of size N can be processed efficiently. In the current implementation N
    .   = 2 ^p^ \* 3 ^q^ \* 5 ^r^ for some integer p, q, r.
    .   
    .   The function returns a negative number if vecsize is too large (very close to INT_MAX ).
    .   
    .   While the function cannot be used directly to estimate the optimal vector size for DCT transform
    .   (since the current DCT implementation supports only even-size vectors), it can be easily processed
    .   as getOptimalDFTSize((vecsize+1)/2)\*2.
    .   @param vecsize vector size.
    .   @sa dft , dct , idft , idct , mulSpectrums
    """
    pass

def getOptimalNewCameraMatrix(cameraMatrix, distCoeffs, imageSize, alpha, newImgSize=None, centerPrincipalPoint=None): # real signature unknown; restored from __doc__
    """
    getOptimalNewCameraMatrix(cameraMatrix, distCoeffs, imageSize, alpha[, newImgSize[, centerPrincipalPoint]]) -> retval, validPixROI
    .   @brief Returns the new camera matrix based on the free scaling parameter.
    .   
    .   @param cameraMatrix Input camera matrix.
    .   @param distCoeffs Input vector of distortion coefficients
    .   \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ of
    .   4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are
    .   assumed.
    .   @param imageSize Original image size.
    .   @param alpha Free scaling parameter between 0 (when all the pixels in the undistorted image are
    .   valid) and 1 (when all the source image pixels are retained in the undistorted image). See
    .   stereoRectify for details.
    .   @param newImgSize Image size after rectification. By default,it is set to imageSize .
    .   @param validPixROI Optional output rectangle that outlines all-good-pixels region in the
    .   undistorted image. See roi1, roi2 description in stereoRectify .
    .   @param centerPrincipalPoint Optional flag that indicates whether in the new camera matrix the
    .   principal point should be at the image center or not. By default, the principal point is chosen to
    .   best fit a subset of the source image (determined by alpha) to the corrected image.
    .   @return new_camera_matrix Output new camera matrix.
    .   
    .   The function computes and returns the optimal new camera matrix based on the free scaling parameter.
    .   By varying this parameter, you may retrieve only sensible pixels alpha=0 , keep all the original
    .   image pixels if there is valuable information in the corners alpha=1 , or get something in between.
    .   When alpha\>0 , the undistortion result is likely to have some black pixels corresponding to
    .   "virtual" pixels outside of the captured distorted image. The original camera matrix, distortion
    .   coefficients, the computed new camera matrix, and newImageSize should be passed to
    .   initUndistortRectifyMap to produce the maps for remap .
    """
    pass

def getPerspectiveTransform(src, dst): # real signature unknown; restored from __doc__
    """
    getPerspectiveTransform(src, dst) -> retval
    .   @brief Calculates a perspective transform from four pairs of the corresponding points.
    .   
    .   The function calculates the \f$3 \times 3\f$ matrix of a perspective transform so that:
    .   
    .   \f[\begin{bmatrix} t_i x'_i \\ t_i y'_i \\ t_i \end{bmatrix} = \texttt{map_matrix} \cdot \begin{bmatrix} x_i \\ y_i \\ 1 \end{bmatrix}\f]
    .   
    .   where
    .   
    .   \f[dst(i)=(x'_i,y'_i), src(i)=(x_i, y_i), i=0,1,2,3\f]
    .   
    .   @param src Coordinates of quadrangle vertices in the source image.
    .   @param dst Coordinates of the corresponding quadrangle vertices in the destination image.
    .   
    .   @sa  findHomography, warpPerspective, perspectiveTransform
    """
    pass

def getRectSubPix(image, patchSize, center, patch=None, patchType=None): # real signature unknown; restored from __doc__
    """
    getRectSubPix(image, patchSize, center[, patch[, patchType]]) -> patch
    .   @brief Retrieves a pixel rectangle from an image with sub-pixel accuracy.
    .   
    .   The function getRectSubPix extracts pixels from src:
    .   
    .   \f[dst(x, y) = src(x +  \texttt{center.x} - ( \texttt{dst.cols} -1)*0.5, y +  \texttt{center.y} - ( \texttt{dst.rows} -1)*0.5)\f]
    .   
    .   where the values of the pixels at non-integer coordinates are retrieved using bilinear
    .   interpolation. Every channel of multi-channel images is processed independently. While the center of
    .   the rectangle must be inside the image, parts of the rectangle may be outside. In this case, the
    .   replication border mode (see cv::BorderTypes) is used to extrapolate the pixel values outside of
    .   the image.
    .   
    .   @param image Source image.
    .   @param patchSize Size of the extracted patch.
    .   @param center Floating point coordinates of the center of the extracted rectangle within the
    .   source image. The center must be inside the image.
    .   @param patch Extracted patch that has the size patchSize and the same number of channels as src .
    .   @param patchType Depth of the extracted pixels. By default, they have the same depth as src .
    .   
    .   @sa  warpAffine, warpPerspective
    """
    pass

def getRotationMatrix2D(center, angle, scale): # real signature unknown; restored from __doc__
    """
    getRotationMatrix2D(center, angle, scale) -> retval
    .   @brief Calculates an affine matrix of 2D rotation.
    .   
    .   The function calculates the following matrix:
    .   
    .   \f[\begin{bmatrix} \alpha &  \beta & (1- \alpha )  \cdot \texttt{center.x} -  \beta \cdot \texttt{center.y} \\ - \beta &  \alpha &  \beta \cdot \texttt{center.x} + (1- \alpha )  \cdot \texttt{center.y} \end{bmatrix}\f]
    .   
    .   where
    .   
    .   \f[\begin{array}{l} \alpha =  \texttt{scale} \cdot \cos \texttt{angle} , \\ \beta =  \texttt{scale} \cdot \sin \texttt{angle} \end{array}\f]
    .   
    .   The transformation maps the rotation center to itself. If this is not the target, adjust the shift.
    .   
    .   @param center Center of the rotation in the source image.
    .   @param angle Rotation angle in degrees. Positive values mean counter-clockwise rotation (the
    .   coordinate origin is assumed to be the top-left corner).
    .   @param scale Isotropic scale factor.
    .   
    .   @sa  getAffineTransform, warpAffine, transform
    """
    pass

def getStructuringElement(shape, ksize, anchor=None): # real signature unknown; restored from __doc__
    """
    getStructuringElement(shape, ksize[, anchor]) -> retval
    .   @brief Returns a structuring element of the specified size and shape for morphological operations.
    .   
    .   The function constructs and returns the structuring element that can be further passed to cv::erode,
    .   cv::dilate or cv::morphologyEx. But you can also construct an arbitrary binary mask yourself and use it as
    .   the structuring element.
    .   
    .   @param shape Element shape that could be one of cv::MorphShapes
    .   @param ksize Size of the structuring element.
    .   @param anchor Anchor position within the element. The default value \f$(-1, -1)\f$ means that the
    .   anchor is at the center. Note that only the shape of a cross-shaped element depends on the anchor
    .   position. In other cases the anchor just regulates how much the result of the morphological
    .   operation is shifted.
    """
    pass

def getTextSize(text, fontFace, fontScale, thickness): # real signature unknown; restored from __doc__
    """
    getTextSize(text, fontFace, fontScale, thickness) -> retval, baseLine
    .   @brief Calculates the width and height of a text string.
    .   
    .   The function getTextSize calculates and returns the size of a box that contains the specified text.
    .   That is, the following code renders some text, the tight box surrounding it, and the baseline: :
    .   @code
    .   String text = "Funny text inside the box";
    .   int fontFace = FONT_HERSHEY_SCRIPT_SIMPLEX;
    .   double fontScale = 2;
    .   int thickness = 3;
    .   
    .   Mat img(600, 800, CV_8UC3, Scalar::all(0));
    .   
    .   int baseline=0;
    .   Size textSize = getTextSize(text, fontFace,
    .   fontScale, thickness, &baseline);
    .   baseline += thickness;
    .   
    .   // center the text
    .   Point textOrg((img.cols - textSize.width)/2,
    .   (img.rows + textSize.height)/2);
    .   
    .   // draw the box
    .   rectangle(img, textOrg + Point(0, baseline),
    .   textOrg + Point(textSize.width, -textSize.height),
    .   Scalar(0,0,255));
    .   // ... and the baseline first
    .   line(img, textOrg + Point(0, thickness),
    .   textOrg + Point(textSize.width, thickness),
    .   Scalar(0, 0, 255));
    .   
    .   // then put the text itself
    .   putText(img, text, textOrg, fontFace, fontScale,
    .   Scalar::all(255), thickness, 8);
    .   @endcode
    .   
    .   @param text Input text string.
    .   @param fontFace Font to use, see cv::HersheyFonts.
    .   @param fontScale Font scale factor that is multiplied by the font-specific base size.
    .   @param thickness Thickness of lines used to render the text. See putText for details.
    .   @param[out] baseLine y-coordinate of the baseline relative to the bottom-most text
    .   point.
    .   @return The size of a box that contains the specified text.
    .   
    .   @see cv::putText
    """
    pass

def getThreadNum(): # real signature unknown; restored from __doc__
    """
    getThreadNum() -> retval
    .   @brief Returns the index of the currently executed thread within the current parallel region. Always
    .   returns 0 if called outside of parallel region.
    .   
    .   The exact meaning of return value depends on the threading framework used by OpenCV library:
    .   - `TBB` - Unsupported with current 4.1 TBB release. Maybe will be supported in future.
    .   - `OpenMP` - The thread number, within the current team, of the calling thread.
    .   - `Concurrency` - An ID for the virtual processor that the current context is executing on (0
    .   for master thread and unique number for others, but not necessary 1,2,3,...).
    .   - `GCD` - System calling thread's ID. Never returns 0 inside parallel region.
    .   - `C=` - The index of the current parallel task.
    .   @sa setNumThreads, getNumThreads
    """
    pass

def getTickCount(): # real signature unknown; restored from __doc__
    """
    getTickCount() -> retval
    .   @brief Returns the number of ticks.
    .   
    .   The function returns the number of ticks after the certain event (for example, when the machine was
    .   turned on). It can be used to initialize RNG or to measure a function execution time by reading the
    .   tick count before and after the function call.
    .   @sa getTickFrequency, TickMeter
    """
    pass

def getTickFrequency(): # real signature unknown; restored from __doc__
    """
    getTickFrequency() -> retval
    .   @brief Returns the number of ticks per second.
    .   
    .   The function returns the number of ticks per second. That is, the following code computes the
    .   execution time in seconds:
    .   @code
    .   double t = (double)getTickCount();
    .   // do something ...
    .   t = ((double)getTickCount() - t)/getTickFrequency();
    .   @endcode
    .   @sa getTickCount, TickMeter
    """
    pass

def getTrackbarPos(trackbarname, winname): # real signature unknown; restored from __doc__
    """
    getTrackbarPos(trackbarname, winname) -> retval
    .   @brief Returns the trackbar position.
    .   
    .   The function returns the current position of the specified trackbar.
    .   
    .   @note
    .   
    .   [__Qt Backend Only__] winname can be empty (or NULL) if the trackbar is attached to the control
    .   panel.
    .   
    .   @param trackbarname Name of the trackbar.
    .   @param winname Name of the window that is the parent of the trackbar.
    """
    pass

def getValidDisparityROI(roi1, roi2, minDisparity, numberOfDisparities, SADWindowSize): # real signature unknown; restored from __doc__
    """
    getValidDisparityROI(roi1, roi2, minDisparity, numberOfDisparities, SADWindowSize) -> retval
    .
    """
    pass

def getWindowProperty(winname, prop_id): # real signature unknown; restored from __doc__
    """
    getWindowProperty(winname, prop_id) -> retval
    .   @brief Provides parameters of a window.
    .   
    .   The function getWindowProperty returns properties of a window.
    .   
    .   @param winname Name of the window.
    .   @param prop_id Window property to retrieve. The following operation flags are available: (cv::WindowPropertyFlags)
    .   
    .   @sa setWindowProperty
    """
    pass

def GFTTDetector_create(maxCorners=None, qualityLevel=None, minDistance=None, blockSize=None, useHarrisDetector=None, k=None): # real signature unknown; restored from __doc__
    """
    GFTTDetector_create([, maxCorners[, qualityLevel[, minDistance[, blockSize[, useHarrisDetector[, k]]]]]]) -> retval
    .
    """
    pass

def goodFeaturesToTrack(image, maxCorners, qualityLevel, minDistance, corners=None, mask=None, blockSize=None, useHarrisDetector=None, k=None): # real signature unknown; restored from __doc__
    """
    goodFeaturesToTrack(image, maxCorners, qualityLevel, minDistance[, corners[, mask[, blockSize[, useHarrisDetector[, k]]]]]) -> corners
    .   @brief Determines strong corners on an image.
    .   
    .   The function finds the most prominent corners in the image or in the specified image region, as
    .   described in @cite Shi94
    .   
    .   -   Function calculates the corner quality measure at every source image pixel using the
    .   cornerMinEigenVal or cornerHarris .
    .   -   Function performs a non-maximum suppression (the local maximums in *3 x 3* neighborhood are
    .   retained).
    .   -   The corners with the minimal eigenvalue less than
    .   \f$\texttt{qualityLevel} \cdot \max_{x,y} qualityMeasureMap(x,y)\f$ are rejected.
    .   -   The remaining corners are sorted by the quality measure in the descending order.
    .   -   Function throws away each corner for which there is a stronger corner at a distance less than
    .   maxDistance.
    .   
    .   The function can be used to initialize a point-based tracker of an object.
    .   
    .   @note If the function is called with different values A and B of the parameter qualityLevel , and
    .   A \> B, the vector of returned corners with qualityLevel=A will be the prefix of the output vector
    .   with qualityLevel=B .
    .   
    .   @param image Input 8-bit or floating-point 32-bit, single-channel image.
    .   @param corners Output vector of detected corners.
    .   @param maxCorners Maximum number of corners to return. If there are more corners than are found,
    .   the strongest of them is returned. `maxCorners <= 0` implies that no limit on the maximum is set
    .   and all detected corners are returned.
    .   @param qualityLevel Parameter characterizing the minimal accepted quality of image corners. The
    .   parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue
    .   (see cornerMinEigenVal ) or the Harris function response (see cornerHarris ). The corners with the
    .   quality measure less than the product are rejected. For example, if the best corner has the
    .   quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure
    .   less than 15 are rejected.
    .   @param minDistance Minimum possible Euclidean distance between the returned corners.
    .   @param mask Optional region of interest. If the image is not empty (it needs to have the type
    .   CV_8UC1 and the same size as image ), it specifies the region in which the corners are detected.
    .   @param blockSize Size of an average block for computing a derivative covariation matrix over each
    .   pixel neighborhood. See cornerEigenValsAndVecs .
    .   @param useHarrisDetector Parameter indicating whether to use a Harris detector (see cornerHarris)
    .   or cornerMinEigenVal.
    .   @param k Free parameter of the Harris detector.
    .   
    .   @sa  cornerMinEigenVal, cornerHarris, calcOpticalFlowPyrLK, estimateRigidTransform,
    """
    pass

def grabCut(img, mask, rect, bgdModel, fgdModel, iterCount, mode=None): # real signature unknown; restored from __doc__
    """
    grabCut(img, mask, rect, bgdModel, fgdModel, iterCount[, mode]) -> mask, bgdModel, fgdModel
    .   @brief Runs the GrabCut algorithm.
    .   
    .   The function implements the [GrabCut image segmentation algorithm](http://en.wikipedia.org/wiki/GrabCut).
    .   
    .   @param img Input 8-bit 3-channel image.
    .   @param mask Input/output 8-bit single-channel mask. The mask is initialized by the function when
    .   mode is set to GC_INIT_WITH_RECT. Its elements may have one of the cv::GrabCutClasses.
    .   @param rect ROI containing a segmented object. The pixels outside of the ROI are marked as
    .   "obvious background". The parameter is only used when mode==GC_INIT_WITH_RECT .
    .   @param bgdModel Temporary array for the background model. Do not modify it while you are
    .   processing the same image.
    .   @param fgdModel Temporary arrays for the foreground model. Do not modify it while you are
    .   processing the same image.
    .   @param iterCount Number of iterations the algorithm should make before returning the result. Note
    .   that the result can be refined with further calls with mode==GC_INIT_WITH_MASK or
    .   mode==GC_EVAL .
    .   @param mode Operation mode that could be one of the cv::GrabCutModes
    """
    pass

def groupRectangles(rectList, groupThreshold, eps=None): # real signature unknown; restored from __doc__
    """
    groupRectangles(rectList, groupThreshold[, eps]) -> rectList, weights
    .   @overload
    """
    pass

def haveOpenVX(): # real signature unknown; restored from __doc__
    """
    haveOpenVX() -> retval
    .
    """
    pass

def hconcat(src, dst=None): # real signature unknown; restored from __doc__
    """
    hconcat(src[, dst]) -> dst
    .   @overload
    .   @code{.cpp}
    .   std::vector<cv::Mat> matrices = { cv::Mat(4, 1, CV_8UC1, cv::Scalar(1)),
    .   cv::Mat(4, 1, CV_8UC1, cv::Scalar(2)),
    .   cv::Mat(4, 1, CV_8UC1, cv::Scalar(3)),};
    .   
    .   cv::Mat out;
    .   cv::hconcat( matrices, out );
    .   //out:
    .   //[1, 2, 3;
    .   // 1, 2, 3;
    .   // 1, 2, 3;
    .   // 1, 2, 3]
    .   @endcode
    .   @param src input array or vector of matrices. all of the matrices must have the same number of rows and the same depth.
    .   @param dst output array. It has the same number of rows and depth as the src, and the sum of cols of the src.
    .   same depth.
    """
    pass

def HOGDescriptor(): # real signature unknown; restored from __doc__
    """
    HOGDescriptor() -> <HOGDescriptor object>
    .   
    
    
    
    HOGDescriptor(_winSize, _blockSize, _blockStride, _cellSize, _nbins[, _derivAperture[, _winSigma[, _histogramNormType[, _L2HysThreshold[, _gammaCorrection[, _nlevels[, _signedGradient]]]]]]]) -> <HOGDescriptor object>
    .   
    
    
    
    HOGDescriptor(filename) -> <HOGDescriptor object>
    .
    """
    pass

def HOGDescriptor_getDaimlerPeopleDetector(): # real signature unknown; restored from __doc__
    """
    HOGDescriptor_getDaimlerPeopleDetector() -> retval
    .
    """
    pass

def HOGDescriptor_getDefaultPeopleDetector(): # real signature unknown; restored from __doc__
    """
    HOGDescriptor_getDefaultPeopleDetector() -> retval
    .
    """
    pass

def HoughCircles(image, method, dp, minDist, circles=None, param1=None, param2=None, minRadius=None, maxRadius=None): # real signature unknown; restored from __doc__
    """
    HoughCircles(image, method, dp, minDist[, circles[, param1[, param2[, minRadius[, maxRadius]]]]]) -> circles
    .   @brief Finds circles in a grayscale image using the Hough transform.
    .   
    .   The function finds circles in a grayscale image using a modification of the Hough transform.
    .   
    .   Example: :
    .   @code
    .   #include <opencv2/imgproc.hpp>
    .   #include <opencv2/highgui.hpp>
    .   #include <math.h>
    .   
    .   using namespace cv;
    .   using namespace std;
    .   
    .   int main(int argc, char** argv)
    .   {
    .   Mat img, gray;
    .   if( argc != 2 || !(img=imread(argv[1], 1)).data)
    .   return -1;
    .   cvtColor(img, gray, COLOR_BGR2GRAY);
    .   // smooth it, otherwise a lot of false circles may be detected
    .   GaussianBlur( gray, gray, Size(9, 9), 2, 2 );
    .   vector<Vec3f> circles;
    .   HoughCircles(gray, circles, HOUGH_GRADIENT,
    .   2, gray.rows/4, 200, 100 );
    .   for( size_t i = 0; i < circles.size(); i++ )
    .   {
    .   Point center(cvRound(circles[i][0]), cvRound(circles[i][1]));
    .   int radius = cvRound(circles[i][2]);
    .   // draw the circle center
    .   circle( img, center, 3, Scalar(0,255,0), -1, 8, 0 );
    .   // draw the circle outline
    .   circle( img, center, radius, Scalar(0,0,255), 3, 8, 0 );
    .   }
    .   namedWindow( "circles", 1 );
    .   imshow( "circles", img );
    .   
    .   waitKey(0);
    .   return 0;
    .   }
    .   @endcode
    .   
    .   @note Usually the function detects the centers of circles well. However, it may fail to find correct
    .   radii. You can assist to the function by specifying the radius range ( minRadius and maxRadius ) if
    .   you know it. Or, you may ignore the returned radius, use only the center, and find the correct
    .   radius using an additional procedure.
    .   
    .   @param image 8-bit, single-channel, grayscale input image.
    .   @param circles Output vector of found circles. Each vector is encoded as a 3-element
    .   floating-point vector \f$(x, y, radius)\f$ .
    .   @param method Detection method, see cv::HoughModes. Currently, the only implemented method is HOUGH_GRADIENT
    .   @param dp Inverse ratio of the accumulator resolution to the image resolution. For example, if
    .   dp=1 , the accumulator has the same resolution as the input image. If dp=2 , the accumulator has
    .   half as big width and height.
    .   @param minDist Minimum distance between the centers of the detected circles. If the parameter is
    .   too small, multiple neighbor circles may be falsely detected in addition to a true one. If it is
    .   too large, some circles may be missed.
    .   @param param1 First method-specific parameter. In case of CV_HOUGH_GRADIENT , it is the higher
    .   threshold of the two passed to the Canny edge detector (the lower one is twice smaller).
    .   @param param2 Second method-specific parameter. In case of CV_HOUGH_GRADIENT , it is the
    .   accumulator threshold for the circle centers at the detection stage. The smaller it is, the more
    .   false circles may be detected. Circles, corresponding to the larger accumulator values, will be
    .   returned first.
    .   @param minRadius Minimum circle radius.
    .   @param maxRadius Maximum circle radius.
    .   
    .   @sa fitEllipse, minEnclosingCircle
    """
    pass

def HoughLines(image, rho, theta, threshold, lines=None, srn=None, stn=None, min_theta=None, max_theta=None): # real signature unknown; restored from __doc__
    """
    HoughLines(image, rho, theta, threshold[, lines[, srn[, stn[, min_theta[, max_theta]]]]]) -> lines
    .   @brief Finds lines in a binary image using the standard Hough transform.
    .   
    .   The function implements the standard or standard multi-scale Hough transform algorithm for line
    .   detection. See <http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm> for a good explanation of Hough
    .   transform.
    .   
    .   @param image 8-bit, single-channel binary source image. The image may be modified by the function.
    .   @param lines Output vector of lines. Each line is represented by a two-element vector
    .   \f$(\rho, \theta)\f$ . \f$\rho\f$ is the distance from the coordinate origin \f$(0,0)\f$ (top-left corner of
    .   the image). \f$\theta\f$ is the line rotation angle in radians (
    .   \f$0 \sim \textrm{vertical line}, \pi/2 \sim \textrm{horizontal line}\f$ ).
    .   @param rho Distance resolution of the accumulator in pixels.
    .   @param theta Angle resolution of the accumulator in radians.
    .   @param threshold Accumulator threshold parameter. Only those lines are returned that get enough
    .   votes ( \f$>\texttt{threshold}\f$ ).
    .   @param srn For the multi-scale Hough transform, it is a divisor for the distance resolution rho .
    .   The coarse accumulator distance resolution is rho and the accurate accumulator resolution is
    .   rho/srn . If both srn=0 and stn=0 , the classical Hough transform is used. Otherwise, both these
    .   parameters should be positive.
    .   @param stn For the multi-scale Hough transform, it is a divisor for the distance resolution theta.
    .   @param min_theta For standard and multi-scale Hough transform, minimum angle to check for lines.
    .   Must fall between 0 and max_theta.
    .   @param max_theta For standard and multi-scale Hough transform, maximum angle to check for lines.
    .   Must fall between min_theta and CV_PI.
    """
    pass

def HoughLinesP(image, rho, theta, threshold, lines=None, minLineLength=None, maxLineGap=None): # real signature unknown; restored from __doc__
    """
    HoughLinesP(image, rho, theta, threshold[, lines[, minLineLength[, maxLineGap]]]) -> lines
    .   @brief Finds line segments in a binary image using the probabilistic Hough transform.
    .   
    .   The function implements the probabilistic Hough transform algorithm for line detection, described
    .   in @cite Matas00
    .   
    .   See the line detection example below:
    .   
    .   @code
    .   #include <opencv2/imgproc.hpp>
    .   #include <opencv2/highgui.hpp>
    .   
    .   using namespace cv;
    .   using namespace std;
    .   
    .   int main(int argc, char** argv)
    .   {
    .   Mat src, dst, color_dst;
    .   if( argc != 2 || !(src=imread(argv[1], 0)).data)
    .   return -1;
    .   
    .   Canny( src, dst, 50, 200, 3 );
    .   cvtColor( dst, color_dst, COLOR_GRAY2BGR );
    .   
    .   #if 0
    .   vector<Vec2f> lines;
    .   HoughLines( dst, lines, 1, CV_PI/180, 100 );
    .   
    .   for( size_t i = 0; i < lines.size(); i++ )
    .   {
    .   float rho = lines[i][0];
    .   float theta = lines[i][1];
    .   double a = cos(theta), b = sin(theta);
    .   double x0 = a*rho, y0 = b*rho;
    .   Point pt1(cvRound(x0 + 1000*(-b)),
    .   cvRound(y0 + 1000*(a)));
    .   Point pt2(cvRound(x0 - 1000*(-b)),
    .   cvRound(y0 - 1000*(a)));
    .   line( color_dst, pt1, pt2, Scalar(0,0,255), 3, 8 );
    .   }
    .   #else
    .   vector<Vec4i> lines;
    .   HoughLinesP( dst, lines, 1, CV_PI/180, 80, 30, 10 );
    .   for( size_t i = 0; i < lines.size(); i++ )
    .   {
    .   line( color_dst, Point(lines[i][0], lines[i][1]),
    .   Point(lines[i][2], lines[i][3]), Scalar(0,0,255), 3, 8 );
    .   }
    .   #endif
    .   namedWindow( "Source", 1 );
    .   imshow( "Source", src );
    .   
    .   namedWindow( "Detected Lines", 1 );
    .   imshow( "Detected Lines", color_dst );
    .   
    .   waitKey(0);
    .   return 0;
    .   }
    .   @endcode
    .   This is a sample picture the function parameters have been tuned for:
    .   
    .   ![image](pics/building.jpg)
    .   
    .   And this is the output of the above program in case of the probabilistic Hough transform:
    .   
    .   ![image](pics/houghp.png)
    .   
    .   @param image 8-bit, single-channel binary source image. The image may be modified by the function.
    .   @param lines Output vector of lines. Each line is represented by a 4-element vector
    .   \f$(x_1, y_1, x_2, y_2)\f$ , where \f$(x_1,y_1)\f$ and \f$(x_2, y_2)\f$ are the ending points of each detected
    .   line segment.
    .   @param rho Distance resolution of the accumulator in pixels.
    .   @param theta Angle resolution of the accumulator in radians.
    .   @param threshold Accumulator threshold parameter. Only those lines are returned that get enough
    .   votes ( \f$>\texttt{threshold}\f$ ).
    .   @param minLineLength Minimum line length. Line segments shorter than that are rejected.
    .   @param maxLineGap Maximum allowed gap between points on the same line to link them.
    .   
    .   @sa LineSegmentDetector
    """
    pass

def HuMoments(m, hu=None): # real signature unknown; restored from __doc__
    """
    HuMoments(m[, hu]) -> hu
    .   @overload
    """
    pass

def idct(src, dst=None, flags=None): # real signature unknown; restored from __doc__
    """
    idct(src[, dst[, flags]]) -> dst
    .   @brief Calculates the inverse Discrete Cosine Transform of a 1D or 2D array.
    .   
    .   idct(src, dst, flags) is equivalent to dct(src, dst, flags | DCT_INVERSE).
    .   @param src input floating-point single-channel array.
    .   @param dst output array of the same size and type as src.
    .   @param flags operation flags.
    .   @sa  dct, dft, idft, getOptimalDFTSize
    """
    pass

def idft(src, dst=None, flags=None, nonzeroRows=None): # real signature unknown; restored from __doc__
    """
    idft(src[, dst[, flags[, nonzeroRows]]]) -> dst
    .   @brief Calculates the inverse Discrete Fourier Transform of a 1D or 2D array.
    .   
    .   idft(src, dst, flags) is equivalent to dft(src, dst, flags | DFT_INVERSE) .
    .   @note None of dft and idft scales the result by default. So, you should pass DFT_SCALE to one of
    .   dft or idft explicitly to make these transforms mutually inverse.
    .   @sa dft, dct, idct, mulSpectrums, getOptimalDFTSize
    .   @param src input floating-point real or complex array.
    .   @param dst output array whose size and type depend on the flags.
    .   @param flags operation flags (see dft and cv::DftFlags).
    .   @param nonzeroRows number of dst rows to process; the rest of the rows have undefined content (see
    .   the convolution sample in dft description.
    """
    pass

def illuminationChange(src, mask, dst=None, alpha=None, beta=None): # real signature unknown; restored from __doc__
    """
    illuminationChange(src, mask[, dst[, alpha[, beta]]]) -> dst
    .   @brief Applying an appropriate non-linear transformation to the gradient field inside the selection and
    .   then integrating back with a Poisson solver, modifies locally the apparent illumination of an image.
    .   
    .   @param src Input 8-bit 3-channel image.
    .   @param mask Input 8-bit 1 or 3-channel image.
    .   @param dst Output image with the same size and type as src.
    .   @param alpha Value ranges between 0-2.
    .   @param beta Value ranges between 0-2.
    .   
    .   This is useful to highlight under-exposed foreground objects or to reduce specular reflections.
    """
    pass

def imdecode(buf, flags): # real signature unknown; restored from __doc__
    """
    imdecode(buf, flags) -> retval
    .   @brief Reads an image from a buffer in memory.
    .   
    .   The function imdecode reads an image from the specified buffer in the memory. If the buffer is too short or
    .   contains invalid data, the function returns an empty matrix ( Mat::data==NULL ).
    .   
    .   See cv::imread for the list of supported formats and flags description.
    .   
    .   @note In the case of color images, the decoded images will have the channels stored in **B G R** order.
    .   @param buf Input array or vector of bytes.
    .   @param flags The same flags as in cv::imread, see cv::ImreadModes.
    """
    pass

def imencode(ext, img, params=None): # real signature unknown; restored from __doc__
    """
    imencode(ext, img[, params]) -> retval, buf
    .   @brief Encodes an image into a memory buffer.
    .   
    .   The function imencode compresses the image and stores it in the memory buffer that is resized to fit the
    .   result. See cv::imwrite for the list of supported formats and flags description.
    .   
    .   @param ext File extension that defines the output format.
    .   @param img Image to be written.
    .   @param buf Output buffer resized to fit the compressed image.
    .   @param params Format-specific parameters. See cv::imwrite and cv::ImwriteFlags.
    """
    pass

def imread(filename, flags=None): # real signature unknown; restored from __doc__
    """
    imread(filename[, flags]) -> retval
    .   @brief Loads an image from a file.
    .   
    .   @anchor imread
    .   
    .   The function imread loads an image from the specified file and returns it. If the image cannot be
    .   read (because of missing file, improper permissions, unsupported or invalid format), the function
    .   returns an empty matrix ( Mat::data==NULL ).
    .   
    .   Currently, the following file formats are supported:
    .   
    .   -   Windows bitmaps - \*.bmp, \*.dib (always supported)
    .   -   JPEG files - \*.jpeg, \*.jpg, \*.jpe (see the *Notes* section)
    .   -   JPEG 2000 files - \*.jp2 (see the *Notes* section)
    .   -   Portable Network Graphics - \*.png (see the *Notes* section)
    .   -   WebP - \*.webp (see the *Notes* section)
    .   -   Portable image format - \*.pbm, \*.pgm, \*.ppm \*.pxm, \*.pnm (always supported)
    .   -   Sun rasters - \*.sr, \*.ras (always supported)
    .   -   TIFF files - \*.tiff, \*.tif (see the *Notes* section)
    .   -   OpenEXR Image files - \*.exr (see the *Notes* section)
    .   -   Radiance HDR - \*.hdr, \*.pic (always supported)
    .   -   Raster and Vector geospatial data supported by Gdal (see the *Notes* section)
    .   
    .   @note
    .   
    .   -   The function determines the type of an image by the content, not by the file extension.
    .   -   In the case of color images, the decoded images will have the channels stored in **B G R** order.
    .   -   On Microsoft Windows\* OS and MacOSX\*, the codecs shipped with an OpenCV image (libjpeg,
    .   libpng, libtiff, and libjasper) are used by default. So, OpenCV can always read JPEGs, PNGs,
    .   and TIFFs. On MacOSX, there is also an option to use native MacOSX image readers. But beware
    .   that currently these native image loaders give images with different pixel values because of
    .   the color management embedded into MacOSX.
    .   -   On Linux\*, BSD flavors and other Unix-like open-source operating systems, OpenCV looks for
    .   codecs supplied with an OS image. Install the relevant packages (do not forget the development
    .   files, for example, "libjpeg-dev", in Debian\* and Ubuntu\*) to get the codec support or turn
    .   on the OPENCV_BUILD_3RDPARTY_LIBS flag in CMake.
    .   -   In the case you set *WITH_GDAL* flag to true in CMake and @ref IMREAD_LOAD_GDAL to load the image,
    .   then [GDAL](http://www.gdal.org) driver will be used in order to decode the image by supporting
    .   the following formats: [Raster](http://www.gdal.org/formats_list.html),
    .   [Vector](http://www.gdal.org/ogr_formats.html).
    .   -   If EXIF information are embedded in the image file, the EXIF orientation will be taken into account
    .   and thus the image will be rotated accordingly except if the flag @ref IMREAD_IGNORE_ORIENTATION is passed.
    .   @param filename Name of file to be loaded.
    .   @param flags Flag that can take values of cv::ImreadModes
    """
    pass

def imreadmulti(filename, mats, flags=None): # real signature unknown; restored from __doc__
    """
    imreadmulti(filename, mats[, flags]) -> retval
    .   @brief Loads a multi-page image from a file.
    .   
    .   The function imreadmulti loads a multi-page image from the specified file into a vector of Mat objects.
    .   @param filename Name of file to be loaded.
    .   @param flags Flag that can take values of cv::ImreadModes, default with cv::IMREAD_ANYCOLOR.
    .   @param mats A vector of Mat objects holding each page, if more than one.
    .   @sa cv::imread
    """
    pass

def imshow(winname, mat): # real signature unknown; restored from __doc__
    """
    imshow(winname, mat) -> None
    .   @brief Displays an image in the specified window.
    .   
    .   The function imshow displays an image in the specified window. If the window was created with the
    .   cv::WINDOW_AUTOSIZE flag, the image is shown with its original size, however it is still limited by the screen resolution.
    .   Otherwise, the image is scaled to fit the window. The function may scale the image, depending on its depth:
    .   
    .   -   If the image is 8-bit unsigned, it is displayed as is.
    .   -   If the image is 16-bit unsigned or 32-bit integer, the pixels are divided by 256. That is, the
    .   value range [0,255\*256] is mapped to [0,255].
    .   -   If the image is 32-bit or 64-bit floating-point, the pixel values are multiplied by 255. That is, the
    .   value range [0,1] is mapped to [0,255].
    .   
    .   If window was created with OpenGL support, cv::imshow also support ogl::Buffer , ogl::Texture2D and
    .   cuda::GpuMat as input.
    .   
    .   If the window was not created before this function, it is assumed creating a window with cv::WINDOW_AUTOSIZE.
    .   
    .   If you need to show an image that is bigger than the screen resolution, you will need to call namedWindow("", WINDOW_NORMAL) before the imshow.
    .   
    .   @note This function should be followed by cv::waitKey function which displays the image for specified
    .   milliseconds. Otherwise, it won't display the image. For example, **waitKey(0)** will display the window
    .   infinitely until any keypress (it is suitable for image display). **waitKey(25)** will display a frame
    .   for 25 ms, after which display will be automatically closed. (If you put it in a loop to read
    .   videos, it will display the video frame-by-frame)
    .   
    .   @note
    .   
    .   [__Windows Backend Only__] Pressing Ctrl+C will copy the image to the clipboard.
    .   
    .   [__Windows Backend Only__] Pressing Ctrl+S will show a dialog to save the image.
    .   
    .   @param winname Name of the window.
    .   @param mat Image to be shown.
    """
    pass

def imwrite(filename, img, params=None): # real signature unknown; restored from __doc__
    """
    imwrite(filename, img[, params]) -> retval
    .   @brief Saves an image to a specified file.
    .   
    .   The function imwrite saves the image to the specified file. The image format is chosen based on the
    .   filename extension (see cv::imread for the list of extensions). Only 8-bit (or 16-bit unsigned (CV_16U)
    .   in case of PNG, JPEG 2000, and TIFF) single-channel or 3-channel (with 'BGR' channel order) images
    .   can be saved using this function. If the format, depth or channel order is different, use
    .   Mat::convertTo , and cv::cvtColor to convert it before saving. Or, use the universal FileStorage I/O
    .   functions to save the image to XML or YAML format.
    .   
    .   It is possible to store PNG images with an alpha channel using this function. To do this, create
    .   8-bit (or 16-bit) 4-channel image BGRA, where the alpha channel goes last. Fully transparent pixels
    .   should have alpha set to 0, fully opaque pixels should have alpha set to 255/65535.
    .   
    .   The sample below shows how to create such a BGRA image and store to PNG file. It also demonstrates how to set custom
    .   compression parameters :
    .   @code
    .   #include <opencv2/opencv.hpp>
    .   
    .   using namespace cv;
    .   using namespace std;
    .   
    .   void createAlphaMat(Mat &mat)
    .   {
    .   CV_Assert(mat.channels() == 4);
    .   for (int i = 0; i < mat.rows; ++i) {
    .   for (int j = 0; j < mat.cols; ++j) {
    .   Vec4b& bgra = mat.at<Vec4b>(i, j);
    .   bgra[0] = UCHAR_MAX; // Blue
    .   bgra[1] = saturate_cast<uchar>((float (mat.cols - j)) / ((float)mat.cols) * UCHAR_MAX); // Green
    .   bgra[2] = saturate_cast<uchar>((float (mat.rows - i)) / ((float)mat.rows) * UCHAR_MAX); // Red
    .   bgra[3] = saturate_cast<uchar>(0.5 * (bgra[1] + bgra[2])); // Alpha
    .   }
    .   }
    .   }
    .   
    .   int main(int argv, char **argc)
    .   {
    .   // Create mat with alpha channel
    .   Mat mat(480, 640, CV_8UC4);
    .   createAlphaMat(mat);
    .   
    .   vector<int> compression_params;
    .   compression_params.push_back(IMWRITE_PNG_COMPRESSION);
    .   compression_params.push_back(9);
    .   
    .   try {
    .   imwrite("alpha.png", mat, compression_params);
    .   }
    .   catch (cv::Exception& ex) {
    .   fprintf(stderr, "Exception converting image to PNG format: %s\n", ex.what());
    .   return 1;
    .   }
    .   
    .   fprintf(stdout, "Saved PNG file with alpha data.\n");
    .   return 0;
    .   }
    .   @endcode
    .   @param filename Name of the file.
    .   @param img Image to be saved.
    .   @param params Format-specific parameters encoded as pairs (paramId_1, paramValue_1, paramId_2, paramValue_2, ... .) see cv::ImwriteFlags
    """
    pass

def initCameraMatrix2D(objectPoints, imagePoints, imageSize, aspectRatio=None): # real signature unknown; restored from __doc__
    """
    initCameraMatrix2D(objectPoints, imagePoints, imageSize[, aspectRatio]) -> retval
    .   @brief Finds an initial camera matrix from 3D-2D point correspondences.
    .   
    .   @param objectPoints Vector of vectors of the calibration pattern points in the calibration pattern
    .   coordinate space. In the old interface all the per-view vectors are concatenated. See
    .   calibrateCamera for details.
    .   @param imagePoints Vector of vectors of the projections of the calibration pattern points. In the
    .   old interface all the per-view vectors are concatenated.
    .   @param imageSize Image size in pixels used to initialize the principal point.
    .   @param aspectRatio If it is zero or negative, both \f$f_x\f$ and \f$f_y\f$ are estimated independently.
    .   Otherwise, \f$f_x = f_y * \texttt{aspectRatio}\f$ .
    .   
    .   The function estimates and returns an initial camera matrix for the camera calibration process.
    .   Currently, the function only supports planar calibration patterns, which are patterns where each
    .   object point has z-coordinate =0.
    """
    pass

def initUndistortRectifyMap(cameraMatrix, distCoeffs, R, newCameraMatrix, size, m1type, map1=None, map2=None): # real signature unknown; restored from __doc__
    """
    initUndistortRectifyMap(cameraMatrix, distCoeffs, R, newCameraMatrix, size, m1type[, map1[, map2]]) -> map1, map2
    .   @brief Computes the undistortion and rectification transformation map.
    .   
    .   The function computes the joint undistortion and rectification transformation and represents the
    .   result in the form of maps for remap. The undistorted image looks like original, as if it is
    .   captured with a camera using the camera matrix =newCameraMatrix and zero distortion. In case of a
    .   monocular camera, newCameraMatrix is usually equal to cameraMatrix, or it can be computed by
    .   cv::getOptimalNewCameraMatrix for a better control over scaling. In case of a stereo camera,
    .   newCameraMatrix is normally set to P1 or P2 computed by cv::stereoRectify .
    .   
    .   Also, this new camera is oriented differently in the coordinate space, according to R. That, for
    .   example, helps to align two heads of a stereo camera so that the epipolar lines on both images
    .   become horizontal and have the same y- coordinate (in case of a horizontally aligned stereo camera).
    .   
    .   The function actually builds the maps for the inverse mapping algorithm that is used by remap. That
    .   is, for each pixel \f$(u, v)\f$ in the destination (corrected and rectified) image, the function
    .   computes the corresponding coordinates in the source image (that is, in the original image from
    .   camera). The following process is applied:
    .   \f[
    .   \begin{array}{l}
    .   x  \leftarrow (u - {c'}_x)/{f'}_x  \\
    .   y  \leftarrow (v - {c'}_y)/{f'}_y  \\
    .   {[X\,Y\,W]} ^T  \leftarrow R^{-1}*[x \, y \, 1]^T  \\
    .   x'  \leftarrow X/W  \\
    .   y'  \leftarrow Y/W  \\
    .   r^2  \leftarrow x'^2 + y'^2 \\
    .   x''  \leftarrow x' \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6}
    .   + 2p_1 x' y' + p_2(r^2 + 2 x'^2)  + s_1 r^2 + s_2 r^4\\
    .   y''  \leftarrow y' \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6}
    .   + p_1 (r^2 + 2 y'^2) + 2 p_2 x' y' + s_3 r^2 + s_4 r^4 \\
    .   s\vecthree{x'''}{y'''}{1} =
    .   \vecthreethree{R_{33}(\tau_x, \tau_y)}{0}{-R_{13}((\tau_x, \tau_y)}
    .   {0}{R_{33}(\tau_x, \tau_y)}{-R_{23}(\tau_x, \tau_y)}
    .   {0}{0}{1} R(\tau_x, \tau_y) \vecthree{x''}{y''}{1}\\
    .   map_x(u,v)  \leftarrow x''' f_x + c_x  \\
    .   map_y(u,v)  \leftarrow y''' f_y + c_y
    .   \end{array}
    .   \f]
    .   where \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$
    .   are the distortion coefficients.
    .   
    .   In case of a stereo camera, this function is called twice: once for each camera head, after
    .   stereoRectify, which in its turn is called after cv::stereoCalibrate. But if the stereo camera
    .   was not calibrated, it is still possible to compute the rectification transformations directly from
    .   the fundamental matrix using cv::stereoRectifyUncalibrated. For each camera, the function computes
    .   homography H as the rectification transformation in a pixel domain, not a rotation matrix R in 3D
    .   space. R can be computed from H as
    .   \f[\texttt{R} = \texttt{cameraMatrix} ^{-1} \cdot \texttt{H} \cdot \texttt{cameraMatrix}\f]
    .   where cameraMatrix can be chosen arbitrarily.
    .   
    .   @param cameraMatrix Input camera matrix \f$A=\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ .
    .   @param distCoeffs Input vector of distortion coefficients
    .   \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$
    .   of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
    .   @param R Optional rectification transformation in the object space (3x3 matrix). R1 or R2 ,
    .   computed by stereoRectify can be passed here. If the matrix is empty, the identity transformation
    .   is assumed. In cvInitUndistortMap R assumed to be an identity matrix.
    .   @param newCameraMatrix New camera matrix \f$A'=\vecthreethree{f_x'}{0}{c_x'}{0}{f_y'}{c_y'}{0}{0}{1}\f$.
    .   @param size Undistorted image size.
    .   @param m1type Type of the first output map that can be CV_32FC1, CV_32FC2 or CV_16SC2, see cv::convertMaps
    .   @param map1 The first output map.
    .   @param map2 The second output map.
    """
    pass

def initWideAngleProjMap(cameraMatrix, distCoeffs, imageSize, destImageWidth, m1type, map1=None, map2=None, projType=None, alpha=None): # real signature unknown; restored from __doc__
    """
    initWideAngleProjMap(cameraMatrix, distCoeffs, imageSize, destImageWidth, m1type[, map1[, map2[, projType[, alpha]]]]) -> retval, map1, map2
    .
    """
    pass

def inpaint(src, inpaintMask, inpaintRadius, flags, dst=None): # real signature unknown; restored from __doc__
    """
    inpaint(src, inpaintMask, inpaintRadius, flags[, dst]) -> dst
    .   @brief Restores the selected region in an image using the region neighborhood.
    .   
    .   @param src Input 8-bit, 16-bit unsigned or 32-bit float 1-channel or 8-bit 3-channel image.
    .   @param inpaintMask Inpainting mask, 8-bit 1-channel image. Non-zero pixels indicate the area that
    .   needs to be inpainted.
    .   @param dst Output image with the same size and type as src .
    .   @param inpaintRadius Radius of a circular neighborhood of each point inpainted that is considered
    .   by the algorithm.
    .   @param flags Inpainting method that could be one of the following:
    .   -   **INPAINT_NS** Navier-Stokes based method [Navier01]
    .   -   **INPAINT_TELEA** Method by Alexandru Telea @cite Telea04 .
    .   
    .   The function reconstructs the selected image area from the pixel near the area boundary. The
    .   function may be used to remove dust and scratches from a scanned photo, or to remove undesirable
    .   objects from still images or video. See <http://en.wikipedia.org/wiki/Inpainting> for more details.
    .   
    .   @note
    .   -   An example using the inpainting technique can be found at
    .   opencv_source_code/samples/cpp/inpaint.cpp
    .   -   (Python) An example using the inpainting technique can be found at
    .   opencv_source_code/samples/python/inpaint.py
    """
    pass

def inRange(src, lowerb, upperb, dst=None): # real signature unknown; restored from __doc__
    """
    inRange(src, lowerb, upperb[, dst]) -> dst
    .   @brief  Checks if array elements lie between the elements of two other arrays.
    .   
    .   The function checks the range as follows:
    .   -   For every element of a single-channel input array:
    .   \f[\texttt{dst} (I)= \texttt{lowerb} (I)_0  \leq \texttt{src} (I)_0 \leq  \texttt{upperb} (I)_0\f]
    .   -   For two-channel arrays:
    .   \f[\texttt{dst} (I)= \texttt{lowerb} (I)_0  \leq \texttt{src} (I)_0 \leq  \texttt{upperb} (I)_0  \land \texttt{lowerb} (I)_1  \leq \texttt{src} (I)_1 \leq  \texttt{upperb} (I)_1\f]
    .   -   and so forth.
    .   
    .   That is, dst (I) is set to 255 (all 1 -bits) if src (I) is within the
    .   specified 1D, 2D, 3D, ... box and 0 otherwise.
    .   
    .   When the lower and/or upper boundary parameters are scalars, the indexes
    .   (I) at lowerb and upperb in the above formulas should be omitted.
    .   @param src first input array.
    .   @param lowerb inclusive lower boundary array or a scalar.
    .   @param upperb inclusive upper boundary array or a scalar.
    .   @param dst output array of the same size as src and CV_8U type.
    """
    pass

def insertChannel(src, dst, coi): # real signature unknown; restored from __doc__
    """
    insertChannel(src, dst, coi) -> dst
    .   @brief Inserts a single channel to dst (coi is 0-based index)
    .   @param src input array
    .   @param dst output array
    .   @param coi index of channel for insertion
    .   @sa mixChannels, merge
    """
    pass

def integral(src, sum=None, sdepth=None): # real signature unknown; restored from __doc__
    """
    integral(src[, sum[, sdepth]]) -> sum
    .   @overload
    """
    pass

def integral2(src, sum=None, sqsum=None, sdepth=None, sqdepth=None): # real signature unknown; restored from __doc__
    """
    integral2(src[, sum[, sqsum[, sdepth[, sqdepth]]]]) -> sum, sqsum
    .   @overload
    """
    pass

def integral3(src, sum=None, sqsum=None, tilted=None, sdepth=None, sqdepth=None): # real signature unknown; restored from __doc__
    """
    integral3(src[, sum[, sqsum[, tilted[, sdepth[, sqdepth]]]]]) -> sum, sqsum, tilted
    .   @brief Calculates the integral of an image.
    .   
    .   The function calculates one or more integral images for the source image as follows:
    .   
    .   \f[\texttt{sum} (X,Y) =  \sum _{x<X,y<Y}  \texttt{image} (x,y)\f]
    .   
    .   \f[\texttt{sqsum} (X,Y) =  \sum _{x<X,y<Y}  \texttt{image} (x,y)^2\f]
    .   
    .   \f[\texttt{tilted} (X,Y) =  \sum _{y<Y,abs(x-X+1) \leq Y-y-1}  \texttt{image} (x,y)\f]
    .   
    .   Using these integral images, you can calculate sum, mean, and standard deviation over a specific
    .   up-right or rotated rectangular region of the image in a constant time, for example:
    .   
    .   \f[\sum _{x_1 \leq x < x_2,  \, y_1  \leq y < y_2}  \texttt{image} (x,y) =  \texttt{sum} (x_2,y_2)- \texttt{sum} (x_1,y_2)- \texttt{sum} (x_2,y_1)+ \texttt{sum} (x_1,y_1)\f]
    .   
    .   It makes possible to do a fast blurring or fast block correlation with a variable window size, for
    .   example. In case of multi-channel images, sums for each channel are accumulated independently.
    .   
    .   As a practical example, the next figure shows the calculation of the integral of a straight
    .   rectangle Rect(3,3,3,2) and of a tilted rectangle Rect(5,1,2,3) . The selected pixels in the
    .   original image are shown, as well as the relative pixels in the integral images sum and tilted .
    .   
    .   ![integral calculation example](pics/integral.png)
    .   
    .   @param src input image as \f$W \times H\f$, 8-bit or floating-point (32f or 64f).
    .   @param sum integral image as \f$(W+1)\times (H+1)\f$ , 32-bit integer or floating-point (32f or 64f).
    .   @param sqsum integral image for squared pixel values; it is \f$(W+1)\times (H+1)\f$, double-precision
    .   floating-point (64f) array.
    .   @param tilted integral for the image rotated by 45 degrees; it is \f$(W+1)\times (H+1)\f$ array with
    .   the same data type as sum.
    .   @param sdepth desired depth of the integral and the tilted integral images, CV_32S, CV_32F, or
    .   CV_64F.
    .   @param sqdepth desired depth of the integral image of squared pixel values, CV_32F or CV_64F.
    """
    pass

def intersectConvexConvex(_p1, _p2, _p12=None, handleNested=None): # real signature unknown; restored from __doc__
    """
    intersectConvexConvex(_p1, _p2[, _p12[, handleNested]]) -> retval, _p12
    .
    """
    pass

def invert(src, dst=None, flags=None): # real signature unknown; restored from __doc__
    """
    invert(src[, dst[, flags]]) -> retval, dst
    .   @brief Finds the inverse or pseudo-inverse of a matrix.
    .   
    .   The function cv::invert inverts the matrix src and stores the result in dst
    .   . When the matrix src is singular or non-square, the function calculates
    .   the pseudo-inverse matrix (the dst matrix) so that norm(src\*dst - I) is
    .   minimal, where I is an identity matrix.
    .   
    .   In case of the DECOMP_LU method, the function returns non-zero value if
    .   the inverse has been successfully calculated and 0 if src is singular.
    .   
    .   In case of the DECOMP_SVD method, the function returns the inverse
    .   condition number of src (the ratio of the smallest singular value to the
    .   largest singular value) and 0 if src is singular. The SVD method
    .   calculates a pseudo-inverse matrix if src is singular.
    .   
    .   Similarly to DECOMP_LU, the method DECOMP_CHOLESKY works only with
    .   non-singular square matrices that should also be symmetrical and
    .   positively defined. In this case, the function stores the inverted
    .   matrix in dst and returns non-zero. Otherwise, it returns 0.
    .   
    .   @param src input floating-point M x N matrix.
    .   @param dst output matrix of N x M size and the same type as src.
    .   @param flags inversion method (cv::DecompTypes)
    .   @sa solve, SVD
    """
    pass

def invertAffineTransform(M, iM=None): # real signature unknown; restored from __doc__
    """
    invertAffineTransform(M[, iM]) -> iM
    .   @brief Inverts an affine transformation.
    .   
    .   The function computes an inverse affine transformation represented by \f$2 \times 3\f$ matrix M:
    .   
    .   \f[\begin{bmatrix} a_{11} & a_{12} & b_1  \\ a_{21} & a_{22} & b_2 \end{bmatrix}\f]
    .   
    .   The result is also a \f$2 \times 3\f$ matrix of the same type as M.
    .   
    .   @param M Original affine transformation.
    .   @param iM Output reverse affine transformation.
    """
    pass

def isContourConvex(contour): # real signature unknown; restored from __doc__
    """
    isContourConvex(contour) -> retval
    .   @brief Tests a contour convexity.
    .   
    .   The function tests whether the input contour is convex or not. The contour must be simple, that is,
    .   without self-intersections. Otherwise, the function output is undefined.
    .   
    .   @param contour Input vector of 2D points, stored in std::vector\<\> or Mat
    """
    pass

def KalmanFilter(dynamParams=None, measureParams=None, controlParams=None, type=None): # real signature unknown; restored from __doc__
    """
    KalmanFilter([dynamParams, measureParams[, controlParams[, type]]]) -> <KalmanFilter object>
    .
    """
    pass

def KAZE_create(extended=None, upright=None, threshold=None, nOctaves=None, nOctaveLayers=None, diffusivity=None): # real signature unknown; restored from __doc__
    """
    KAZE_create([, extended[, upright[, threshold[, nOctaves[, nOctaveLayers[, diffusivity]]]]]]) -> retval
    .   @brief The KAZE constructor
    .   
    .   @param extended Set to enable extraction of extended (128-byte) descriptor.
    .   @param upright Set to enable use of upright descriptors (non rotation-invariant).
    .   @param threshold Detector response threshold to accept point
    .   @param nOctaves Maximum octave evolution of the image
    .   @param nOctaveLayers Default number of sublevels per scale level
    .   @param diffusivity Diffusivity type. DIFF_PM_G1, DIFF_PM_G2, DIFF_WEICKERT or
    .   DIFF_CHARBONNIER
    """
    pass

def KeyPoint(x=None, y=None, _size=None, _angle=None, _response=None, _octave=None, _class_id=None): # real signature unknown; restored from __doc__
    """
    KeyPoint([x, y, _size[, _angle[, _response[, _octave[, _class_id]]]]]) -> <KeyPoint object>
    .
    """
    pass

def KeyPoint_convert(keypoints, keypointIndexes=None): # real signature unknown; restored from __doc__
    """
    KeyPoint_convert(keypoints[, keypointIndexes]) -> points2f
    .   This method converts vector of keypoints to vector of points or the reverse, where each keypoint is
    .   assigned the same size and the same orientation.
    .   
    .   @param keypoints Keypoints obtained from any feature detection algorithm like SIFT/SURF/ORB
    .   @param points2f Array of (x,y) coordinates of each keypoint
    .   @param keypointIndexes Array of indexes of keypoints to be converted to points. (Acts like a mask to
    .   convert only specified keypoints)
    
    
    
    KeyPoint_convert(points2f[, size[, response[, octave[, class_id]]]]) -> keypoints
    .   @overload
    .   @param points2f Array of (x,y) coordinates of each keypoint
    .   @param keypoints Keypoints obtained from any feature detection algorithm like SIFT/SURF/ORB
    .   @param size keypoint diameter
    .   @param response keypoint detector response on the keypoint (that is, strength of the keypoint)
    .   @param octave pyramid octave in which the keypoint has been detected
    .   @param class_id object id
    """
    pass

def KeyPoint_overlap(kp1, kp2): # real signature unknown; restored from __doc__
    """
    KeyPoint_overlap(kp1, kp2) -> retval
    .   This method computes overlap for pair of keypoints. Overlap is the ratio between area of keypoint
    .   regions' intersection and area of keypoint regions' union (considering keypoint region as circle).
    .   If they don't overlap, we get zero. If they coincide at same location with same size, we get 1.
    .   @param kp1 First keypoint
    .   @param kp2 Second keypoint
    """
    pass

def kmeans(data, K, bestLabels, criteria, attempts, flags, centers=None): # real signature unknown; restored from __doc__
    """
    kmeans(data, K, bestLabels, criteria, attempts, flags[, centers]) -> retval, bestLabels, centers
    .   @brief Finds centers of clusters and groups input samples around the clusters.
    .   
    .   The function kmeans implements a k-means algorithm that finds the centers of cluster_count clusters
    .   and groups the input samples around the clusters. As an output, \f$\texttt{labels}_i\f$ contains a
    .   0-based cluster index for the sample stored in the \f$i^{th}\f$ row of the samples matrix.
    .   
    .   @note
    .   -   (Python) An example on K-means clustering can be found at
    .   opencv_source_code/samples/python/kmeans.py
    .   @param data Data for clustering. An array of N-Dimensional points with float coordinates is needed.
    .   Examples of this array can be:
    .   -   Mat points(count, 2, CV_32F);
    .   -   Mat points(count, 1, CV_32FC2);
    .   -   Mat points(1, count, CV_32FC2);
    .   -   std::vector\<cv::Point2f\> points(sampleCount);
    .   @param K Number of clusters to split the set by.
    .   @param bestLabels Input/output integer array that stores the cluster indices for every sample.
    .   @param criteria The algorithm termination criteria, that is, the maximum number of iterations and/or
    .   the desired accuracy. The accuracy is specified as criteria.epsilon. As soon as each of the cluster
    .   centers moves by less than criteria.epsilon on some iteration, the algorithm stops.
    .   @param attempts Flag to specify the number of times the algorithm is executed using different
    .   initial labellings. The algorithm returns the labels that yield the best compactness (see the last
    .   function parameter).
    .   @param flags Flag that can take values of cv::KmeansFlags
    .   @param centers Output matrix of the cluster centers, one row per each cluster center.
    .   @return The function returns the compactness measure that is computed as
    .   \f[\sum _i  \| \texttt{samples} _i -  \texttt{centers} _{ \texttt{labels} _i} \| ^2\f]
    .   after every attempt. The best (minimum) value is chosen and the corresponding labels and the
    .   compactness value are returned by the function. Basically, you can use only the core of the
    .   function, set the number of attempts to 1, initialize labels each time using a custom algorithm,
    .   pass them with the ( flags = KMEANS_USE_INITIAL_LABELS ) flag, and then choose the best
    .   (most-compact) clustering.
    """
    pass

def Laplacian(src, ddepth, dst=None, ksize=None, scale=None, delta=None, borderType=None): # real signature unknown; restored from __doc__
    """
    Laplacian(src, ddepth[, dst[, ksize[, scale[, delta[, borderType]]]]]) -> dst
    .   @brief Calculates the Laplacian of an image.
    .   
    .   The function calculates the Laplacian of the source image by adding up the second x and y
    .   derivatives calculated using the Sobel operator:
    .   
    .   \f[\texttt{dst} =  \Delta \texttt{src} =  \frac{\partial^2 \texttt{src}}{\partial x^2} +  \frac{\partial^2 \texttt{src}}{\partial y^2}\f]
    .   
    .   This is done when `ksize > 1`. When `ksize == 1`, the Laplacian is computed by filtering the image
    .   with the following \f$3 \times 3\f$ aperture:
    .   
    .   \f[\vecthreethree {0}{1}{0}{1}{-4}{1}{0}{1}{0}\f]
    .   
    .   @param src Source image.
    .   @param dst Destination image of the same size and the same number of channels as src .
    .   @param ddepth Desired depth of the destination image.
    .   @param ksize Aperture size used to compute the second-derivative filters. See getDerivKernels for
    .   details. The size must be positive and odd.
    .   @param scale Optional scale factor for the computed Laplacian values. By default, no scaling is
    .   applied. See getDerivKernels for details.
    .   @param delta Optional delta value that is added to the results prior to storing them in dst .
    .   @param borderType Pixel extrapolation method, see cv::BorderTypes
    .   @sa  Sobel, Scharr
    """
    pass

def line(img, pt1, pt2, color, thickness=None, lineType=None, shift=None): # real signature unknown; restored from __doc__
    """
    line(img, pt1, pt2, color[, thickness[, lineType[, shift]]]) -> img
    .   @brief Draws a line segment connecting two points.
    .   
    .   The function line draws the line segment between pt1 and pt2 points in the image. The line is
    .   clipped by the image boundaries. For non-antialiased lines with integer coordinates, the 8-connected
    .   or 4-connected Bresenham algorithm is used. Thick lines are drawn with rounding endings. Antialiased
    .   lines are drawn using Gaussian filtering.
    .   
    .   @param img Image.
    .   @param pt1 First point of the line segment.
    .   @param pt2 Second point of the line segment.
    .   @param color Line color.
    .   @param thickness Line thickness.
    .   @param lineType Type of the line, see cv::LineTypes.
    .   @param shift Number of fractional bits in the point coordinates.
    """
    pass

def linearPolar(src, center, maxRadius, flags, dst=None): # real signature unknown; restored from __doc__
    """
    linearPolar(src, center, maxRadius, flags[, dst]) -> dst
    .   @brief Remaps an image to polar coordinates space.
    .   
    .   @anchor polar_remaps_reference_image
    .   ![Polar remaps reference](pics/polar_remap_doc.png)
    .   
    .   Transform the source image using the following transformation:
    .   \f[\begin{array}{l}
    .   dst( \rho , \phi ) = src(x,y) \\
    .   dst.size() \leftarrow src.size()
    .   \end{array}\f]
    .   
    .   where
    .   \f[\begin{array}{l}
    .   I = (dx,dy) = (x - center.x,y - center.y) \\
    .   \rho = Kx \cdot \texttt{magnitude} (I) ,\\
    .   \phi = Ky \cdot \texttt{angle} (I)_{0..360 deg}
    .   \end{array}\f]
    .   
    .   and
    .   \f[\begin{array}{l}
    .   Kx = src.cols / maxRadius \\
    .   Ky = src.rows / 360
    .   \end{array}\f]
    .   
    .   
    .   @param src Source image
    .   @param dst Destination image. It will have same size and type as src.
    .   @param center The transformation center;
    .   @param maxRadius The radius of the bounding circle to transform. It determines the inverse magnitude scale parameter too.
    .   @param flags A combination of interpolation methods, see cv::InterpolationFlags
    .   
    .   @note
    .   -   The function can not operate in-place.
    .   -   To calculate magnitude and angle in degrees @ref cv::cartToPolar is used internally thus angles are measured from 0 to 360 with accuracy about 0.3 degrees.
    """
    pass

def log(src, dst=None): # real signature unknown; restored from __doc__
    """
    log(src[, dst]) -> dst
    .   @brief Calculates the natural logarithm of every array element.
    .   
    .   The function cv::log calculates the natural logarithm of every element of the input array:
    .   \f[\texttt{dst} (I) =  \log (\texttt{src}(I)) \f]
    .   
    .   Output on zero, negative and special (NaN, Inf) values is undefined.
    .   
    .   @param src input array.
    .   @param dst output array of the same size and type as src .
    .   @sa exp, cartToPolar, polarToCart, phase, pow, sqrt, magnitude
    """
    pass

def logPolar(src, center, M, flags, dst=None): # real signature unknown; restored from __doc__
    """
    logPolar(src, center, M, flags[, dst]) -> dst
    .   @brief Remaps an image to semilog-polar coordinates space.
    .   
    .   Transform the source image using the following transformation (See @ref polar_remaps_reference_image "Polar remaps reference image"):
    .   \f[\begin{array}{l}
    .   dst( \rho , \phi ) = src(x,y) \\
    .   dst.size() \leftarrow src.size()
    .   \end{array}\f]
    .   
    .   where
    .   \f[\begin{array}{l}
    .   I = (dx,dy) = (x - center.x,y - center.y) \\
    .   \rho = M \cdot log_e(\texttt{magnitude} (I)) ,\\
    .   \phi = Ky \cdot \texttt{angle} (I)_{0..360 deg} \\
    .   \end{array}\f]
    .   
    .   and
    .   \f[\begin{array}{l}
    .   M = src.cols / log_e(maxRadius) \\
    .   Ky = src.rows / 360 \\
    .   \end{array}\f]
    .   
    .   The function emulates the human "foveal" vision and can be used for fast scale and
    .   rotation-invariant template matching, for object tracking and so forth.
    .   @param src Source image
    .   @param dst Destination image. It will have same size and type as src.
    .   @param center The transformation center; where the output precision is maximal
    .   @param M Magnitude scale parameter. It determines the radius of the bounding circle to transform too.
    .   @param flags A combination of interpolation methods, see cv::InterpolationFlags
    .   
    .   @note
    .   -   The function can not operate in-place.
    .   -   To calculate magnitude and angle in degrees @ref cv::cartToPolar is used internally thus angles are measured from 0 to 360 with accuracy about 0.3 degrees.
    """
    pass

def LUT(src, lut, dst=None): # real signature unknown; restored from __doc__
    """
    LUT(src, lut[, dst]) -> dst
    .   @brief Performs a look-up table transform of an array.
    .   
    .   The function LUT fills the output array with values from the look-up table. Indices of the entries
    .   are taken from the input array. That is, the function processes each element of src as follows:
    .   \f[\texttt{dst} (I)  \leftarrow \texttt{lut(src(I) + d)}\f]
    .   where
    .   \f[d =  \fork{0}{if \(\texttt{src}\) has depth \(\texttt{CV_8U}\)}{128}{if \(\texttt{src}\) has depth \(\texttt{CV_8S}\)}\f]
    .   @param src input array of 8-bit elements.
    .   @param lut look-up table of 256 elements; in case of multi-channel input array, the table should
    .   either have a single channel (in this case the same table is used for all channels) or the same
    .   number of channels as in the input array.
    .   @param dst output array of the same size and number of channels as src, and the same depth as lut.
    .   @sa  convertScaleAbs, Mat::convertTo
    """
    pass

def magnitude(x, y, magnitude=None): # real signature unknown; restored from __doc__
    """
    magnitude(x, y[, magnitude]) -> magnitude
    .   @brief Calculates the magnitude of 2D vectors.
    .   
    .   The function cv::magnitude calculates the magnitude of 2D vectors formed
    .   from the corresponding elements of x and y arrays:
    .   \f[\texttt{dst} (I) =  \sqrt{\texttt{x}(I)^2 + \texttt{y}(I)^2}\f]
    .   @param x floating-point array of x-coordinates of the vectors.
    .   @param y floating-point array of y-coordinates of the vectors; it must
    .   have the same size as x.
    .   @param magnitude output array of the same size and type as x.
    .   @sa cartToPolar, polarToCart, phase, sqrt
    """
    pass

def Mahalanobis(v1, v2, icovar): # real signature unknown; restored from __doc__
    """
    Mahalanobis(v1, v2, icovar) -> retval
    .   @brief Calculates the Mahalanobis distance between two vectors.
    .   
    .   The function cv::Mahalanobis calculates and returns the weighted distance between two vectors:
    .   \f[d( \texttt{vec1} , \texttt{vec2} )= \sqrt{\sum_{i,j}{\texttt{icovar(i,j)}\cdot(\texttt{vec1}(I)-\texttt{vec2}(I))\cdot(\texttt{vec1(j)}-\texttt{vec2(j)})} }\f]
    .   The covariance matrix may be calculated using the cv::calcCovarMatrix function and then inverted using
    .   the invert function (preferably using the cv::DECOMP_SVD method, as the most accurate).
    .   @param v1 first 1D input vector.
    .   @param v2 second 1D input vector.
    .   @param icovar inverse covariance matrix.
    """
    pass

def matchShapes(contour1, contour2, method, parameter): # real signature unknown; restored from __doc__
    """
    matchShapes(contour1, contour2, method, parameter) -> retval
    .   @brief Compares two shapes.
    .   
    .   The function compares two shapes. All three implemented methods use the Hu invariants (see cv::HuMoments)
    .   
    .   @param contour1 First contour or grayscale image.
    .   @param contour2 Second contour or grayscale image.
    .   @param method Comparison method, see cv::ShapeMatchModes
    .   @param parameter Method-specific parameter (not supported now).
    """
    pass

def matchTemplate(image, templ, method, result=None, mask=None): # real signature unknown; restored from __doc__
    """
    matchTemplate(image, templ, method[, result[, mask]]) -> result
    .   @brief Compares a template against overlapped image regions.
    .   
    .   The function slides through image , compares the overlapped patches of size \f$w \times h\f$ against
    .   templ using the specified method and stores the comparison results in result . Here are the formulae
    .   for the available comparison methods ( \f$I\f$ denotes image, \f$T\f$ template, \f$R\f$ result ). The summation
    .   is done over template and/or the image patch: \f$x' = 0...w-1, y' = 0...h-1\f$
    .   
    .   After the function finishes the comparison, the best matches can be found as global minimums (when
    .   TM_SQDIFF was used) or maximums (when TM_CCORR or TM_CCOEFF was used) using the
    .   minMaxLoc function. In case of a color image, template summation in the numerator and each sum in
    .   the denominator is done over all of the channels and separate mean values are used for each channel.
    .   That is, the function can take a color template and a color image. The result will still be a
    .   single-channel image, which is easier to analyze.
    .   
    .   @param image Image where the search is running. It must be 8-bit or 32-bit floating-point.
    .   @param templ Searched template. It must be not greater than the source image and have the same
    .   data type.
    .   @param result Map of comparison results. It must be single-channel 32-bit floating-point. If image
    .   is \f$W \times H\f$ and templ is \f$w \times h\f$ , then result is \f$(W-w+1) \times (H-h+1)\f$ .
    .   @param method Parameter specifying the comparison method, see cv::TemplateMatchModes
    .   @param mask Mask of searched template. It must have the same datatype and size with templ. It is
    .   not set by default. Currently, only the TM_SQDIFF and TM_CCORR_NORMED methods are supported.
    """
    pass

def matMulDeriv(A, B, dABdA=None, dABdB=None): # real signature unknown; restored from __doc__
    """
    matMulDeriv(A, B[, dABdA[, dABdB]]) -> dABdA, dABdB
    .   @brief Computes partial derivatives of the matrix product for each multiplied matrix.
    .   
    .   @param A First multiplied matrix.
    .   @param B Second multiplied matrix.
    .   @param dABdA First output derivative matrix d(A\*B)/dA of size
    .   \f$\texttt{A.rows*B.cols} \times {A.rows*A.cols}\f$ .
    .   @param dABdB Second output derivative matrix d(A\*B)/dB of size
    .   \f$\texttt{A.rows*B.cols} \times {B.rows*B.cols}\f$ .
    .   
    .   The function computes partial derivatives of the elements of the matrix product \f$A*B\f$ with regard to
    .   the elements of each of the two input matrices. The function is used to compute the Jacobian
    .   matrices in stereoCalibrate but can also be used in any other similar optimization function.
    """
    pass

def max(src1, src2, dst=None): # real signature unknown; restored from __doc__
    """
    max(src1, src2[, dst]) -> dst
    .   @brief Calculates per-element maximum of two arrays or an array and a scalar.
    .   
    .   The function cv::max calculates the per-element maximum of two arrays:
    .   \f[\texttt{dst} (I)= \max ( \texttt{src1} (I), \texttt{src2} (I))\f]
    .   or array and a scalar:
    .   \f[\texttt{dst} (I)= \max ( \texttt{src1} (I), \texttt{value} )\f]
    .   @param src1 first input array.
    .   @param src2 second input array of the same size and type as src1 .
    .   @param dst output array of the same size and type as src1.
    .   @sa  min, compare, inRange, minMaxLoc, @ref MatrixExpressions
    """
    pass

def mean(src, mask=None): # real signature unknown; restored from __doc__
    """
    mean(src[, mask]) -> retval
    .   @brief Calculates an average (mean) of array elements.
    .   
    .   The function cv::mean calculates the mean value M of array elements,
    .   independently for each channel, and return it:
    .   \f[\begin{array}{l} N =  \sum _{I: \; \texttt{mask} (I) \ne 0} 1 \\ M_c =  \left ( \sum _{I: \; \texttt{mask} (I) \ne 0}{ \texttt{mtx} (I)_c} \right )/N \end{array}\f]
    .   When all the mask elements are 0's, the function returns Scalar::all(0)
    .   @param src input array that should have from 1 to 4 channels so that the result can be stored in
    .   Scalar_ .
    .   @param mask optional operation mask.
    .   @sa  countNonZero, meanStdDev, norm, minMaxLoc
    """
    pass

def meanShift(probImage, window, criteria): # real signature unknown; restored from __doc__
    """
    meanShift(probImage, window, criteria) -> retval, window
    .   @brief Finds an object on a back projection image.
    .   
    .   @param probImage Back projection of the object histogram. See calcBackProject for details.
    .   @param window Initial search window.
    .   @param criteria Stop criteria for the iterative search algorithm.
    .   returns
    .   :   Number of iterations CAMSHIFT took to converge.
    .   The function implements the iterative object search algorithm. It takes the input back projection of
    .   an object and the initial position. The mass center in window of the back projection image is
    .   computed and the search window center shifts to the mass center. The procedure is repeated until the
    .   specified number of iterations criteria.maxCount is done or until the window center shifts by less
    .   than criteria.epsilon. The algorithm is used inside CamShift and, unlike CamShift , the search
    .   window size or orientation do not change during the search. You can simply pass the output of
    .   calcBackProject to this function. But better results can be obtained if you pre-filter the back
    .   projection and remove the noise. For example, you can do this by retrieving connected components
    .   with findContours , throwing away contours with small area ( contourArea ), and rendering the
    .   remaining contours with drawContours.
    """
    pass

def meanStdDev(src, mean=None, stddev=None, mask=None): # real signature unknown; restored from __doc__
    """
    meanStdDev(src[, mean[, stddev[, mask]]]) -> mean, stddev
    .   Calculates a mean and standard deviation of array elements.
    .   
    .   The function cv::meanStdDev calculates the mean and the standard deviation M
    .   of array elements independently for each channel and returns it via the
    .   output parameters:
    .   \f[\begin{array}{l} N =  \sum _{I, \texttt{mask} (I)  \ne 0} 1 \\ \texttt{mean} _c =  \frac{\sum_{ I: \; \texttt{mask}(I) \ne 0} \texttt{src} (I)_c}{N} \\ \texttt{stddev} _c =  \sqrt{\frac{\sum_{ I: \; \texttt{mask}(I) \ne 0} \left ( \texttt{src} (I)_c -  \texttt{mean} _c \right )^2}{N}} \end{array}\f]
    .   When all the mask elements are 0's, the function returns
    .   mean=stddev=Scalar::all(0).
    .   @note The calculated standard deviation is only the diagonal of the
    .   complete normalized covariance matrix. If the full matrix is needed, you
    .   can reshape the multi-channel array M x N to the single-channel array
    .   M\*N x mtx.channels() (only possible when the matrix is continuous) and
    .   then pass the matrix to calcCovarMatrix .
    .   @param src input array that should have from 1 to 4 channels so that the results can be stored in
    .   Scalar_ 's.
    .   @param mean output parameter: calculated mean value.
    .   @param stddev output parameter: calculated standard deviation.
    .   @param mask optional operation mask.
    .   @sa  countNonZero, mean, norm, minMaxLoc, calcCovarMatrix
    """
    pass

def medianBlur(src, ksize, dst=None): # real signature unknown; restored from __doc__
    """
    medianBlur(src, ksize[, dst]) -> dst
    .   @brief Blurs an image using the median filter.
    .   
    .   The function smoothes an image using the median filter with the \f$\texttt{ksize} \times
    .   \texttt{ksize}\f$ aperture. Each channel of a multi-channel image is processed independently.
    .   In-place operation is supported.
    .   
    .   @note The median filter uses BORDER_REPLICATE internally to cope with border pixels, see cv::BorderTypes
    .   
    .   @param src input 1-, 3-, or 4-channel image; when ksize is 3 or 5, the image depth should be
    .   CV_8U, CV_16U, or CV_32F, for larger aperture sizes, it can only be CV_8U.
    .   @param dst destination array of the same size and type as src.
    .   @param ksize aperture linear size; it must be odd and greater than 1, for example: 3, 5, 7 ...
    .   @sa  bilateralFilter, blur, boxFilter, GaussianBlur
    """
    pass

def merge(mv, dst=None): # real signature unknown; restored from __doc__
    """
    merge(mv[, dst]) -> dst
    .   @overload
    .   @param mv input vector of matrices to be merged; all the matrices in mv must have the same
    .   size and the same depth.
    .   @param dst output array of the same size and the same depth as mv[0]; The number of channels will
    .   be the total number of channels in the matrix array.
    """
    pass

def min(src1, src2, dst=None): # real signature unknown; restored from __doc__
    """
    min(src1, src2[, dst]) -> dst
    .   @brief Calculates per-element minimum of two arrays or an array and a scalar.
    .   
    .   The function cv::min calculates the per-element minimum of two arrays:
    .   \f[\texttt{dst} (I)= \min ( \texttt{src1} (I), \texttt{src2} (I))\f]
    .   or array and a scalar:
    .   \f[\texttt{dst} (I)= \min ( \texttt{src1} (I), \texttt{value} )\f]
    .   @param src1 first input array.
    .   @param src2 second input array of the same size and type as src1.
    .   @param dst output array of the same size and type as src1.
    .   @sa max, compare, inRange, minMaxLoc
    """
    pass

def minAreaRect(points): # real signature unknown; restored from __doc__
    """
    minAreaRect(points) -> retval
    .   @brief Finds a rotated rectangle of the minimum area enclosing the input 2D point set.
    .   
    .   The function calculates and returns the minimum-area bounding rectangle (possibly rotated) for a
    .   specified point set. See the OpenCV sample minarea.cpp . Developer should keep in mind that the
    .   returned rotatedRect can contain negative indices when data is close to the containing Mat element
    .   boundary.
    .   
    .   @param points Input vector of 2D points, stored in std::vector\<\> or Mat
    """
    pass

def minEnclosingCircle(points): # real signature unknown; restored from __doc__
    """
    minEnclosingCircle(points) -> center, radius
    .   @brief Finds a circle of the minimum area enclosing a 2D point set.
    .   
    .   The function finds the minimal enclosing circle of a 2D point set using an iterative algorithm. See
    .   the OpenCV sample minarea.cpp .
    .   
    .   @param points Input vector of 2D points, stored in std::vector\<\> or Mat
    .   @param center Output center of the circle.
    .   @param radius Output radius of the circle.
    """
    pass

def minEnclosingTriangle(points, triangle=None): # real signature unknown; restored from __doc__
    """
    minEnclosingTriangle(points[, triangle]) -> retval, triangle
    .   @brief Finds a triangle of minimum area enclosing a 2D point set and returns its area.
    .   
    .   The function finds a triangle of minimum area enclosing the given set of 2D points and returns its
    .   area. The output for a given 2D point set is shown in the image below. 2D points are depicted in
    .   *red* and the enclosing triangle in *yellow*.
    .   
    .   ![Sample output of the minimum enclosing triangle function](pics/minenclosingtriangle.png)
    .   
    .   The implementation of the algorithm is based on O'Rourke's @cite ORourke86 and Klee and Laskowski's
    .   @cite KleeLaskowski85 papers. O'Rourke provides a \f$\theta(n)\f$ algorithm for finding the minimal
    .   enclosing triangle of a 2D convex polygon with n vertices. Since the minEnclosingTriangle function
    .   takes a 2D point set as input an additional preprocessing step of computing the convex hull of the
    .   2D point set is required. The complexity of the convexHull function is \f$O(n log(n))\f$ which is higher
    .   than \f$\theta(n)\f$. Thus the overall complexity of the function is \f$O(n log(n))\f$.
    .   
    .   @param points Input vector of 2D points with depth CV_32S or CV_32F, stored in std::vector\<\> or Mat
    .   @param triangle Output vector of three 2D points defining the vertices of the triangle. The depth
    .   of the OutputArray must be CV_32F.
    """
    pass

def minMaxLoc(src, mask=None): # real signature unknown; restored from __doc__
    """
    minMaxLoc(src[, mask]) -> minVal, maxVal, minLoc, maxLoc
    .   @brief Finds the global minimum and maximum in an array.
    .   
    .   The function cv::minMaxLoc finds the minimum and maximum element values and their positions. The
    .   extremums are searched across the whole array or, if mask is not an empty array, in the specified
    .   array region.
    .   
    .   The function do not work with multi-channel arrays. If you need to find minimum or maximum
    .   elements across all the channels, use Mat::reshape first to reinterpret the array as
    .   single-channel. Or you may extract the particular channel using either extractImageCOI , or
    .   mixChannels , or split .
    .   @param src input single-channel array.
    .   @param minVal pointer to the returned minimum value; NULL is used if not required.
    .   @param maxVal pointer to the returned maximum value; NULL is used if not required.
    .   @param minLoc pointer to the returned minimum location (in 2D case); NULL is used if not required.
    .   @param maxLoc pointer to the returned maximum location (in 2D case); NULL is used if not required.
    .   @param mask optional mask used to select a sub-array.
    .   @sa max, min, compare, inRange, extractImageCOI, mixChannels, split, Mat::reshape
    """
    pass

def mixChannels(src, dst, fromTo): # real signature unknown; restored from __doc__
    """
    mixChannels(src, dst, fromTo) -> dst
    .   @overload
    .   @param src input array or vector of matrices; all of the matrices must have the same size and the
    .   same depth.
    .   @param dst output array or vector of matrices; all the matrices **must be allocated**; their size and
    .   depth must be the same as in src[0].
    .   @param fromTo array of index pairs specifying which channels are copied and where; fromTo[k\*2] is
    .   a 0-based index of the input channel in src, fromTo[k\*2+1] is an index of the output channel in
    .   dst; the continuous channel numbering is used: the first input image channels are indexed from 0 to
    .   src[0].channels()-1, the second input image channels are indexed from src[0].channels() to
    .   src[0].channels() + src[1].channels()-1, and so on, the same scheme is used for the output image
    .   channels; as a special case, when fromTo[k\*2] is negative, the corresponding output channel is
    .   filled with zero .
    """
    pass

def moments(array, binaryImage=None): # real signature unknown; restored from __doc__
    """
    moments(array[, binaryImage]) -> retval
    .   @brief Calculates all of the moments up to the third order of a polygon or rasterized shape.
    .   
    .   The function computes moments, up to the 3rd order, of a vector shape or a rasterized shape. The
    .   results are returned in the structure cv::Moments.
    .   
    .   @param array Raster image (single-channel, 8-bit or floating-point 2D array) or an array (
    .   \f$1 \times N\f$ or \f$N \times 1\f$ ) of 2D points (Point or Point2f ).
    .   @param binaryImage If it is true, all non-zero image pixels are treated as 1's. The parameter is
    .   used for images only.
    .   @returns moments.
    .   
    .   @note Only applicable to contour moments calculations from Python bindings: Note that the numpy
    .   type for the input array should be either np.int32 or np.float32.
    .   
    .   @sa  contourArea, arcLength
    """
    pass

def morphologyEx(src, op, kernel, dst=None, anchor=None, iterations=None, borderType=None, borderValue=None): # real signature unknown; restored from __doc__
    """
    morphologyEx(src, op, kernel[, dst[, anchor[, iterations[, borderType[, borderValue]]]]]) -> dst
    .   @brief Performs advanced morphological transformations.
    .   
    .   The function morphologyEx can perform advanced morphological transformations using an erosion and dilation as
    .   basic operations.
    .   
    .   Any of the operations can be done in-place. In case of multi-channel images, each channel is
    .   processed independently.
    .   
    .   @param src Source image. The number of channels can be arbitrary. The depth should be one of
    .   CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
    .   @param dst Destination image of the same size and type as source image.
    .   @param op Type of a morphological operation, see cv::MorphTypes
    .   @param kernel Structuring element. It can be created using cv::getStructuringElement.
    .   @param anchor Anchor position with the kernel. Negative values mean that the anchor is at the
    .   kernel center.
    .   @param iterations Number of times erosion and dilation are applied.
    .   @param borderType Pixel extrapolation method, see cv::BorderTypes
    .   @param borderValue Border value in case of a constant border. The default value has a special
    .   meaning.
    .   @sa  dilate, erode, getStructuringElement
    .   @note The number of iterations is the number of times erosion or dilatation operation will be applied.
    .   For instance, an opening operation (#MORPH_OPEN) with two iterations is equivalent to apply
    .   successively: erode -> erode -> dilate -> dilate (and not erode -> dilate -> erode -> dilate).
    """
    pass

def moveWindow(winname, x, y): # real signature unknown; restored from __doc__
    """
    moveWindow(winname, x, y) -> None
    .   @brief Moves window to the specified position
    .   
    .   @param winname Name of the window.
    .   @param x The new x-coordinate of the window.
    .   @param y The new y-coordinate of the window.
    """
    pass

def MSER_create(_delta=None, _min_area=None, _max_area=None, _max_variation=None, _min_diversity=None, _max_evolution=None, _area_threshold=None, _min_margin=None, _edge_blur_size=None): # real signature unknown; restored from __doc__
    """
    MSER_create([, _delta[, _min_area[, _max_area[, _max_variation[, _min_diversity[, _max_evolution[, _area_threshold[, _min_margin[, _edge_blur_size]]]]]]]]]) -> retval
    .   @brief Full consturctor for %MSER detector
    .   
    .   @param _delta it compares \f$(size_{i}-size_{i-delta})/size_{i-delta}\f$
    .   @param _min_area prune the area which smaller than minArea
    .   @param _max_area prune the area which bigger than maxArea
    .   @param _max_variation prune the area have simliar size to its children
    .   @param _min_diversity for color image, trace back to cut off mser with diversity less than min_diversity
    .   @param _max_evolution  for color image, the evolution steps
    .   @param _area_threshold for color image, the area threshold to cause re-initialize
    .   @param _min_margin for color image, ignore too small margin
    .   @param _edge_blur_size for color image, the aperture size for edge blur
    """
    pass

def mulSpectrums(a, b, flags, c=None, conjB=None): # real signature unknown; restored from __doc__
    """
    mulSpectrums(a, b, flags[, c[, conjB]]) -> c
    .   @brief Performs the per-element multiplication of two Fourier spectrums.
    .   
    .   The function cv::mulSpectrums performs the per-element multiplication of the two CCS-packed or complex
    .   matrices that are results of a real or complex Fourier transform.
    .   
    .   The function, together with dft and idft , may be used to calculate convolution (pass conjB=false )
    .   or correlation (pass conjB=true ) of two arrays rapidly. When the arrays are complex, they are
    .   simply multiplied (per element) with an optional conjugation of the second-array elements. When the
    .   arrays are real, they are assumed to be CCS-packed (see dft for details).
    .   @param a first input array.
    .   @param b second input array of the same size and type as src1 .
    .   @param c output array of the same size and type as src1 .
    .   @param flags operation flags; currently, the only supported flag is cv::DFT_ROWS, which indicates that
    .   each row of src1 and src2 is an independent 1D Fourier spectrum. If you do not want to use this flag, then simply add a `0` as value.
    .   @param conjB optional flag that conjugates the second input array before the multiplication (true)
    .   or not (false).
    """
    pass

def multiply(src1, src2, dst=None, scale=None, dtype=None): # real signature unknown; restored from __doc__
    """
    multiply(src1, src2[, dst[, scale[, dtype]]]) -> dst
    .   @brief Calculates the per-element scaled product of two arrays.
    .   
    .   The function multiply calculates the per-element product of two arrays:
    .   
    .   \f[\texttt{dst} (I)= \texttt{saturate} ( \texttt{scale} \cdot \texttt{src1} (I)  \cdot \texttt{src2} (I))\f]
    .   
    .   There is also a @ref MatrixExpressions -friendly variant of the first function. See Mat::mul .
    .   
    .   For a not-per-element matrix product, see gemm .
    .   
    .   @note Saturation is not applied when the output array has the depth
    .   CV_32S. You may even get result of an incorrect sign in the case of
    .   overflow.
    .   @param src1 first input array.
    .   @param src2 second input array of the same size and the same type as src1.
    .   @param dst output array of the same size and type as src1.
    .   @param scale optional scale factor.
    .   @param dtype optional depth of the output array
    .   @sa add, subtract, divide, scaleAdd, addWeighted, accumulate, accumulateProduct, accumulateSquare,
    .   Mat::convertTo
    """
    pass

def mulTransposed(src, aTa, dst=None, delta=None, scale=None, dtype=None): # real signature unknown; restored from __doc__
    """
    mulTransposed(src, aTa[, dst[, delta[, scale[, dtype]]]]) -> dst
    .   @brief Calculates the product of a matrix and its transposition.
    .   
    .   The function cv::mulTransposed calculates the product of src and its
    .   transposition:
    .   \f[\texttt{dst} = \texttt{scale} ( \texttt{src} - \texttt{delta} )^T ( \texttt{src} - \texttt{delta} )\f]
    .   if aTa=true , and
    .   \f[\texttt{dst} = \texttt{scale} ( \texttt{src} - \texttt{delta} ) ( \texttt{src} - \texttt{delta} )^T\f]
    .   otherwise. The function is used to calculate the covariance matrix. With
    .   zero delta, it can be used as a faster substitute for general matrix
    .   product A\*B when B=A'
    .   @param src input single-channel matrix. Note that unlike gemm, the
    .   function can multiply not only floating-point matrices.
    .   @param dst output square matrix.
    .   @param aTa Flag specifying the multiplication ordering. See the
    .   description below.
    .   @param delta Optional delta matrix subtracted from src before the
    .   multiplication. When the matrix is empty ( delta=noArray() ), it is
    .   assumed to be zero, that is, nothing is subtracted. If it has the same
    .   size as src , it is simply subtracted. Otherwise, it is "repeated" (see
    .   repeat ) to cover the full src and then subtracted. Type of the delta
    .   matrix, when it is not empty, must be the same as the type of created
    .   output matrix. See the dtype parameter description below.
    .   @param scale Optional scale factor for the matrix product.
    .   @param dtype Optional type of the output matrix. When it is negative,
    .   the output matrix will have the same type as src . Otherwise, it will be
    .   type=CV_MAT_DEPTH(dtype) that should be either CV_32F or CV_64F .
    .   @sa calcCovarMatrix, gemm, repeat, reduce
    """
    pass

def namedWindow(winname, flags=None): # real signature unknown; restored from __doc__
    """
    namedWindow(winname[, flags]) -> None
    .   @brief Creates a window.
    .   
    .   The function namedWindow creates a window that can be used as a placeholder for images and
    .   trackbars. Created windows are referred to by their names.
    .   
    .   If a window with the same name already exists, the function does nothing.
    .   
    .   You can call cv::destroyWindow or cv::destroyAllWindows to close the window and de-allocate any associated
    .   memory usage. For a simple program, you do not really have to call these functions because all the
    .   resources and windows of the application are closed automatically by the operating system upon exit.
    .   
    .   @note
    .   
    .   Qt backend supports additional flags:
    .   -   **WINDOW_NORMAL or WINDOW_AUTOSIZE:** WINDOW_NORMAL enables you to resize the
    .   window, whereas WINDOW_AUTOSIZE adjusts automatically the window size to fit the
    .   displayed image (see imshow ), and you cannot change the window size manually.
    .   -   **WINDOW_FREERATIO or WINDOW_KEEPRATIO:** WINDOW_FREERATIO adjusts the image
    .   with no respect to its ratio, whereas WINDOW_KEEPRATIO keeps the image ratio.
    .   -   **WINDOW_GUI_NORMAL or WINDOW_GUI_EXPANDED:** WINDOW_GUI_NORMAL is the old way to draw the window
    .   without statusbar and toolbar, whereas WINDOW_GUI_EXPANDED is a new enhanced GUI.
    .   By default, flags == WINDOW_AUTOSIZE | WINDOW_KEEPRATIO | WINDOW_GUI_EXPANDED
    .   
    .   @param winname Name of the window in the window caption that may be used as a window identifier.
    .   @param flags Flags of the window. The supported flags are: (cv::WindowFlags)
    """
    pass

def norm(src1, normType=None, mask=None): # real signature unknown; restored from __doc__
    """
    norm(src1[, normType[, mask]]) -> retval
    .   @brief Calculates an absolute array norm, an absolute difference norm, or a
    .   relative difference norm.
    .   
    .   The function cv::norm calculates an absolute norm of src1 (when there is no
    .   src2 ):
    .   
    .   \f[norm =  \forkthree{\|\texttt{src1}\|_{L_{\infty}} =  \max _I | \texttt{src1} (I)|}{if  \(\texttt{normType} = \texttt{NORM_INF}\) }
    .   { \| \texttt{src1} \| _{L_1} =  \sum _I | \texttt{src1} (I)|}{if  \(\texttt{normType} = \texttt{NORM_L1}\) }
    .   { \| \texttt{src1} \| _{L_2} =  \sqrt{\sum_I \texttt{src1}(I)^2} }{if  \(\texttt{normType} = \texttt{NORM_L2}\) }\f]
    .   
    .   or an absolute or relative difference norm if src2 is there:
    .   
    .   \f[norm =  \forkthree{\|\texttt{src1}-\texttt{src2}\|_{L_{\infty}} =  \max _I | \texttt{src1} (I) -  \texttt{src2} (I)|}{if  \(\texttt{normType} = \texttt{NORM_INF}\) }
    .   { \| \texttt{src1} - \texttt{src2} \| _{L_1} =  \sum _I | \texttt{src1} (I) -  \texttt{src2} (I)|}{if  \(\texttt{normType} = \texttt{NORM_L1}\) }
    .   { \| \texttt{src1} - \texttt{src2} \| _{L_2} =  \sqrt{\sum_I (\texttt{src1}(I) - \texttt{src2}(I))^2} }{if  \(\texttt{normType} = \texttt{NORM_L2}\) }\f]
    .   
    .   or
    .   
    .   \f[norm =  \forkthree{\frac{\|\texttt{src1}-\texttt{src2}\|_{L_{\infty}}    }{\|\texttt{src2}\|_{L_{\infty}} }}{if  \(\texttt{normType} = \texttt{NORM_RELATIVE_INF}\) }
    .   { \frac{\|\texttt{src1}-\texttt{src2}\|_{L_1} }{\|\texttt{src2}\|_{L_1}} }{if  \(\texttt{normType} = \texttt{NORM_RELATIVE_L1}\) }
    .   { \frac{\|\texttt{src1}-\texttt{src2}\|_{L_2} }{\|\texttt{src2}\|_{L_2}} }{if  \(\texttt{normType} = \texttt{NORM_RELATIVE_L2}\) }\f]
    .   
    .   The function cv::norm returns the calculated norm.
    .   
    .   When the mask parameter is specified and it is not empty, the norm is
    .   calculated only over the region specified by the mask.
    .   
    .   A multi-channel input arrays are treated as a single-channel, that is,
    .   the results for all channels are combined.
    .   
    .   @param src1 first input array.
    .   @param normType type of the norm (see cv::NormTypes).
    .   @param mask optional operation mask; it must have the same size as src1 and CV_8UC1 type.
    
    
    
    norm(src1, src2[, normType[, mask]]) -> retval
    .   @overload
    .   @param src1 first input array.
    .   @param src2 second input array of the same size and the same type as src1.
    .   @param normType type of the norm (cv::NormTypes).
    .   @param mask optional operation mask; it must have the same size as src1 and CV_8UC1 type.
    """
    pass

def normalize(src, dst, alpha=None, beta=None, norm_type=None, dtype=None, mask=None): # real signature unknown; restored from __doc__
    """
    normalize(src, dst[, alpha[, beta[, norm_type[, dtype[, mask]]]]]) -> dst
    .   @brief Normalizes the norm or value range of an array.
    .   
    .   The function cv::normalize normalizes scale and shift the input array elements so that
    .   \f[\| \texttt{dst} \| _{L_p}= \texttt{alpha}\f]
    .   (where p=Inf, 1 or 2) when normType=NORM_INF, NORM_L1, or NORM_L2, respectively; or so that
    .   \f[\min _I  \texttt{dst} (I)= \texttt{alpha} , \, \, \max _I  \texttt{dst} (I)= \texttt{beta}\f]
    .   
    .   when normType=NORM_MINMAX (for dense arrays only). The optional mask specifies a sub-array to be
    .   normalized. This means that the norm or min-n-max are calculated over the sub-array, and then this
    .   sub-array is modified to be normalized. If you want to only use the mask to calculate the norm or
    .   min-max but modify the whole array, you can use norm and Mat::convertTo.
    .   
    .   In case of sparse matrices, only the non-zero values are analyzed and transformed. Because of this,
    .   the range transformation for sparse matrices is not allowed since it can shift the zero level.
    .   
    .   Possible usage with some positive example data:
    .   @code{.cpp}
    .   vector<double> positiveData = { 2.0, 8.0, 10.0 };
    .   vector<double> normalizedData_l1, normalizedData_l2, normalizedData_inf, normalizedData_minmax;
    .   
    .   // Norm to probability (total count)
    .   // sum(numbers) = 20.0
    .   // 2.0      0.1     (2.0/20.0)
    .   // 8.0      0.4     (8.0/20.0)
    .   // 10.0     0.5     (10.0/20.0)
    .   normalize(positiveData, normalizedData_l1, 1.0, 0.0, NORM_L1);
    .   
    .   // Norm to unit vector: ||positiveData|| = 1.0
    .   // 2.0      0.15
    .   // 8.0      0.62
    .   // 10.0     0.77
    .   normalize(positiveData, normalizedData_l2, 1.0, 0.0, NORM_L2);
    .   
    .   // Norm to max element
    .   // 2.0      0.2     (2.0/10.0)
    .   // 8.0      0.8     (8.0/10.0)
    .   // 10.0     1.0     (10.0/10.0)
    .   normalize(positiveData, normalizedData_inf, 1.0, 0.0, NORM_INF);
    .   
    .   // Norm to range [0.0;1.0]
    .   // 2.0      0.0     (shift to left border)
    .   // 8.0      0.75    (6.0/8.0)
    .   // 10.0     1.0     (shift to right border)
    .   normalize(positiveData, normalizedData_minmax, 1.0, 0.0, NORM_MINMAX);
    .   @endcode
    .   
    .   @param src input array.
    .   @param dst output array of the same size as src .
    .   @param alpha norm value to normalize to or the lower range boundary in case of the range
    .   normalization.
    .   @param beta upper range boundary in case of the range normalization; it is not used for the norm
    .   normalization.
    .   @param norm_type normalization type (see cv::NormTypes).
    .   @param dtype when negative, the output array has the same type as src; otherwise, it has the same
    .   number of channels as src and the depth =CV_MAT_DEPTH(dtype).
    .   @param mask optional operation mask.
    .   @sa norm, Mat::convertTo, SparseMat::convertTo
    """
    pass

def ORB_create(nfeatures=None, scaleFactor=None, nlevels=None, edgeThreshold=None, firstLevel=None, WTA_K=None, scoreType=None, patchSize=None, fastThreshold=None): # real signature unknown; restored from __doc__
    """
    ORB_create([, nfeatures[, scaleFactor[, nlevels[, edgeThreshold[, firstLevel[, WTA_K[, scoreType[, patchSize[, fastThreshold]]]]]]]]]) -> retval
    .   @brief The ORB constructor
    .   
    .   @param nfeatures The maximum number of features to retain.
    .   @param scaleFactor Pyramid decimation ratio, greater than 1. scaleFactor==2 means the classical
    .   pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor
    .   will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor
    .   will mean that to cover certain scale range you will need more pyramid levels and so the speed
    .   will suffer.
    .   @param nlevels The number of pyramid levels. The smallest level will have linear size equal to
    .   input_image_linear_size/pow(scaleFactor, nlevels).
    .   @param edgeThreshold This is size of the border where the features are not detected. It should
    .   roughly match the patchSize parameter.
    .   @param firstLevel It should be 0 in the current implementation.
    .   @param WTA_K The number of points that produce each element of the oriented BRIEF descriptor. The
    .   default value 2 means the BRIEF where we take a random point pair and compare their brightnesses,
    .   so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3
    .   random points (of course, those point coordinates are random, but they are generated from the
    .   pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel
    .   rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such
    .   output will occupy 2 bits, and therefore it will need a special variant of Hamming distance,
    .   denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each
    .   bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3).
    .   @param scoreType The default HARRIS_SCORE means that Harris algorithm is used to rank features
    .   (the score is written to KeyPoint::score and is used to retain best nfeatures features);
    .   FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints,
    .   but it is a little faster to compute.
    .   @param patchSize size of the patch used by the oriented BRIEF descriptor. Of course, on smaller
    .   pyramid layers the perceived image area covered by a feature will be larger.
    .   @param fastThreshold
    """
    pass

def patchNaNs(a, val=None): # real signature unknown; restored from __doc__
    """
    patchNaNs(a[, val]) -> a
    .   @brief converts NaN's to the given number
    """
    pass

def PCABackProject(data, mean, eigenvectors, result=None): # real signature unknown; restored from __doc__
    """
    PCABackProject(data, mean, eigenvectors[, result]) -> result
    .   wrap PCA::backProject
    """
    pass

def PCACompute(data, mean, eigenvectors=None, maxComponents=None): # real signature unknown; restored from __doc__
    """
    PCACompute(data, mean[, eigenvectors[, maxComponents]]) -> mean, eigenvectors
    .   wrap PCA::operator()
    
    
    
    PCACompute(data, mean, retainedVariance[, eigenvectors]) -> mean, eigenvectors
    .   wrap PCA::operator()
    """
    pass

def PCAProject(data, mean, eigenvectors, result=None): # real signature unknown; restored from __doc__
    """
    PCAProject(data, mean, eigenvectors[, result]) -> result
    .   wrap PCA::project
    """
    pass

def pencilSketch(src, dst1=None, dst2=None, sigma_s=None, sigma_r=None, shade_factor=None): # real signature unknown; restored from __doc__
    """
    pencilSketch(src[, dst1[, dst2[, sigma_s[, sigma_r[, shade_factor]]]]]) -> dst1, dst2
    .   @brief Pencil-like non-photorealistic line drawing
    .   
    .   @param src Input 8-bit 3-channel image.
    .   @param dst1 Output 8-bit 1-channel image.
    .   @param dst2 Output image with the same size and type as src.
    .   @param sigma_s Range between 0 to 200.
    .   @param sigma_r Range between 0 to 1.
    .   @param shade_factor Range between 0 to 0.1.
    """
    pass

def perspectiveTransform(src, m, dst=None): # real signature unknown; restored from __doc__
    """
    perspectiveTransform(src, m[, dst]) -> dst
    .   @brief Performs the perspective matrix transformation of vectors.
    .   
    .   The function cv::perspectiveTransform transforms every element of src by
    .   treating it as a 2D or 3D vector, in the following way:
    .   \f[(x, y, z)  \rightarrow (x'/w, y'/w, z'/w)\f]
    .   where
    .   \f[(x', y', z', w') =  \texttt{mat} \cdot \begin{bmatrix} x & y & z & 1  \end{bmatrix}\f]
    .   and
    .   \f[w =  \fork{w'}{if \(w' \ne 0\)}{\infty}{otherwise}\f]
    .   
    .   Here a 3D vector transformation is shown. In case of a 2D vector
    .   transformation, the z component is omitted.
    .   
    .   @note The function transforms a sparse set of 2D or 3D vectors. If you
    .   want to transform an image using perspective transformation, use
    .   warpPerspective . If you have an inverse problem, that is, you want to
    .   compute the most probable perspective transformation out of several
    .   pairs of corresponding points, you can use getPerspectiveTransform or
    .   findHomography .
    .   @param src input two-channel or three-channel floating-point array; each
    .   element is a 2D/3D vector to be transformed.
    .   @param dst output array of the same size and type as src.
    .   @param m 3x3 or 4x4 floating-point transformation matrix.
    .   @sa  transform, warpPerspective, getPerspectiveTransform, findHomography
    """
    pass

def phase(x, y, angle=None, angleInDegrees=None): # real signature unknown; restored from __doc__
    """
    phase(x, y[, angle[, angleInDegrees]]) -> angle
    .   @brief Calculates the rotation angle of 2D vectors.
    .   
    .   The function cv::phase calculates the rotation angle of each 2D vector that
    .   is formed from the corresponding elements of x and y :
    .   \f[\texttt{angle} (I) =  \texttt{atan2} ( \texttt{y} (I), \texttt{x} (I))\f]
    .   
    .   The angle estimation accuracy is about 0.3 degrees. When x(I)=y(I)=0 ,
    .   the corresponding angle(I) is set to 0.
    .   @param x input floating-point array of x-coordinates of 2D vectors.
    .   @param y input array of y-coordinates of 2D vectors; it must have the
    .   same size and the same type as x.
    .   @param angle output array of vector angles; it has the same size and
    .   same type as x .
    .   @param angleInDegrees when true, the function calculates the angle in
    .   degrees, otherwise, they are measured in radians.
    """
    pass

def phaseCorrelate(src1, src2, window=None): # real signature unknown; restored from __doc__
    """
    phaseCorrelate(src1, src2[, window]) -> retval, response
    .   @brief The function is used to detect translational shifts that occur between two images.
    .   
    .   The operation takes advantage of the Fourier shift theorem for detecting the translational shift in
    .   the frequency domain. It can be used for fast image registration as well as motion estimation. For
    .   more information please see <http://en.wikipedia.org/wiki/Phase_correlation>
    .   
    .   Calculates the cross-power spectrum of two supplied source arrays. The arrays are padded if needed
    .   with getOptimalDFTSize.
    .   
    .   The function performs the following equations:
    .   - First it applies a Hanning window (see <http://en.wikipedia.org/wiki/Hann_function>) to each
    .   image to remove possible edge effects. This window is cached until the array size changes to speed
    .   up processing time.
    .   - Next it computes the forward DFTs of each source array:
    .   \f[\mathbf{G}_a = \mathcal{F}\{src_1\}, \; \mathbf{G}_b = \mathcal{F}\{src_2\}\f]
    .   where \f$\mathcal{F}\f$ is the forward DFT.
    .   - It then computes the cross-power spectrum of each frequency domain array:
    .   \f[R = \frac{ \mathbf{G}_a \mathbf{G}_b^*}{|\mathbf{G}_a \mathbf{G}_b^*|}\f]
    .   - Next the cross-correlation is converted back into the time domain via the inverse DFT:
    .   \f[r = \mathcal{F}^{-1}\{R\}\f]
    .   - Finally, it computes the peak location and computes a 5x5 weighted centroid around the peak to
    .   achieve sub-pixel accuracy.
    .   \f[(\Delta x, \Delta y) = \texttt{weightedCentroid} \{\arg \max_{(x, y)}\{r\}\}\f]
    .   - If non-zero, the response parameter is computed as the sum of the elements of r within the 5x5
    .   centroid around the peak location. It is normalized to a maximum of 1 (meaning there is a single
    .   peak) and will be smaller when there are multiple peaks.
    .   
    .   @param src1 Source floating point array (CV_32FC1 or CV_64FC1)
    .   @param src2 Source floating point array (CV_32FC1 or CV_64FC1)
    .   @param window Floating point array with windowing coefficients to reduce edge effects (optional).
    .   @param response Signal power within the 5x5 centroid around the peak, between 0 and 1 (optional).
    .   @returns detected phase shift (sub-pixel) between the two arrays.
    .   
    .   @sa dft, getOptimalDFTSize, idft, mulSpectrums createHanningWindow
    """
    pass

def pointPolygonTest(contour, pt, measureDist): # real signature unknown; restored from __doc__
    """
    pointPolygonTest(contour, pt, measureDist) -> retval
    .   @brief Performs a point-in-contour test.
    .   
    .   The function determines whether the point is inside a contour, outside, or lies on an edge (or
    .   coincides with a vertex). It returns positive (inside), negative (outside), or zero (on an edge)
    .   value, correspondingly. When measureDist=false , the return value is +1, -1, and 0, respectively.
    .   Otherwise, the return value is a signed distance between the point and the nearest contour edge.
    .   
    .   See below a sample output of the function where each image pixel is tested against the contour:
    .   
    .   ![sample output](pics/pointpolygon.png)
    .   
    .   @param contour Input contour.
    .   @param pt Point tested against the contour.
    .   @param measureDist If true, the function estimates the signed distance from the point to the
    .   nearest contour edge. Otherwise, the function only checks if the point is inside a contour or not.
    """
    pass

def polarToCart(magnitude, angle, x=None, y=None, angleInDegrees=None): # real signature unknown; restored from __doc__
    """
    polarToCart(magnitude, angle[, x[, y[, angleInDegrees]]]) -> x, y
    .   @brief Calculates x and y coordinates of 2D vectors from their magnitude and angle.
    .   
    .   The function cv::polarToCart calculates the Cartesian coordinates of each 2D
    .   vector represented by the corresponding elements of magnitude and angle:
    .   \f[\begin{array}{l} \texttt{x} (I) =  \texttt{magnitude} (I) \cos ( \texttt{angle} (I)) \\ \texttt{y} (I) =  \texttt{magnitude} (I) \sin ( \texttt{angle} (I)) \\ \end{array}\f]
    .   
    .   The relative accuracy of the estimated coordinates is about 1e-6.
    .   @param magnitude input floating-point array of magnitudes of 2D vectors;
    .   it can be an empty matrix (=Mat()), in this case, the function assumes
    .   that all the magnitudes are =1; if it is not empty, it must have the
    .   same size and type as angle.
    .   @param angle input floating-point array of angles of 2D vectors.
    .   @param x output array of x-coordinates of 2D vectors; it has the same
    .   size and type as angle.
    .   @param y output array of y-coordinates of 2D vectors; it has the same
    .   size and type as angle.
    .   @param angleInDegrees when true, the input angles are measured in
    .   degrees, otherwise, they are measured in radians.
    .   @sa cartToPolar, magnitude, phase, exp, log, pow, sqrt
    """
    pass

def polylines(img, pts, isClosed, color, thickness=None, lineType=None, shift=None): # real signature unknown; restored from __doc__
    """
    polylines(img, pts, isClosed, color[, thickness[, lineType[, shift]]]) -> img
    .   @brief Draws several polygonal curves.
    .   
    .   @param img Image.
    .   @param pts Array of polygonal curves.
    .   @param isClosed Flag indicating whether the drawn polylines are closed or not. If they are closed,
    .   the function draws a line from the last vertex of each curve to its first vertex.
    .   @param color Polyline color.
    .   @param thickness Thickness of the polyline edges.
    .   @param lineType Type of the line segments. See the line description.
    .   @param shift Number of fractional bits in the vertex coordinates.
    .   
    .   The function polylines draws one or more polygonal curves.
    """
    pass

def pow(src, power, dst=None): # real signature unknown; restored from __doc__
    """
    pow(src, power[, dst]) -> dst
    .   @brief Raises every array element to a power.
    .   
    .   The function cv::pow raises every element of the input array to power :
    .   \f[\texttt{dst} (I) =  \fork{\texttt{src}(I)^{power}}{if \(\texttt{power}\) is integer}{|\texttt{src}(I)|^{power}}{otherwise}\f]
    .   
    .   So, for a non-integer power exponent, the absolute values of input array
    .   elements are used. However, it is possible to get true values for
    .   negative values using some extra operations. In the example below,
    .   computing the 5th root of array src shows:
    .   @code{.cpp}
    .   Mat mask = src < 0;
    .   pow(src, 1./5, dst);
    .   subtract(Scalar::all(0), dst, dst, mask);
    .   @endcode
    .   For some values of power, such as integer values, 0.5 and -0.5,
    .   specialized faster algorithms are used.
    .   
    .   Special values (NaN, Inf) are not handled.
    .   @param src input array.
    .   @param power exponent of power.
    .   @param dst output array of the same size and type as src.
    .   @sa sqrt, exp, log, cartToPolar, polarToCart
    """
    pass

def preCornerDetect(src, ksize, dst=None, borderType=None): # real signature unknown; restored from __doc__
    """
    preCornerDetect(src, ksize[, dst[, borderType]]) -> dst
    .   @brief Calculates a feature map for corner detection.
    .   
    .   The function calculates the complex spatial derivative-based function of the source image
    .   
    .   \f[\texttt{dst} = (D_x  \texttt{src} )^2  \cdot D_{yy}  \texttt{src} + (D_y  \texttt{src} )^2  \cdot D_{xx}  \texttt{src} - 2 D_x  \texttt{src} \cdot D_y  \texttt{src} \cdot D_{xy}  \texttt{src}\f]
    .   
    .   where \f$D_x\f$,\f$D_y\f$ are the first image derivatives, \f$D_{xx}\f$,\f$D_{yy}\f$ are the second image
    .   derivatives, and \f$D_{xy}\f$ is the mixed derivative.
    .   
    .   The corners can be found as local maximums of the functions, as shown below:
    .   @code
    .   Mat corners, dilated_corners;
    .   preCornerDetect(image, corners, 3);
    .   // dilation with 3x3 rectangular structuring element
    .   dilate(corners, dilated_corners, Mat(), 1);
    .   Mat corner_mask = corners == dilated_corners;
    .   @endcode
    .   
    .   @param src Source single-channel 8-bit of floating-point image.
    .   @param dst Output image that has the type CV_32F and the same size as src .
    .   @param ksize %Aperture size of the Sobel .
    .   @param borderType Pixel extrapolation method. See cv::BorderTypes.
    """
    pass

def projectPoints(objectPoints, rvec, tvec, cameraMatrix, distCoeffs, imagePoints=None, jacobian=None, aspectRatio=None): # real signature unknown; restored from __doc__
    """
    projectPoints(objectPoints, rvec, tvec, cameraMatrix, distCoeffs[, imagePoints[, jacobian[, aspectRatio]]]) -> imagePoints, jacobian
    .   @brief Projects 3D points to an image plane.
    .   
    .   @param objectPoints Array of object points, 3xN/Nx3 1-channel or 1xN/Nx1 3-channel (or
    .   vector\<Point3f\> ), where N is the number of points in the view.
    .   @param rvec Rotation vector. See Rodrigues for details.
    .   @param tvec Translation vector.
    .   @param cameraMatrix Camera matrix \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{_1}\f$ .
    .   @param distCoeffs Input vector of distortion coefficients
    .   \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ of
    .   4, 5, 8, 12 or 14 elements. If the vector is empty, the zero distortion coefficients are assumed.
    .   @param imagePoints Output array of image points, 2xN/Nx2 1-channel or 1xN/Nx1 2-channel, or
    .   vector\<Point2f\> .
    .   @param jacobian Optional output 2Nx(10+\<numDistCoeffs\>) jacobian matrix of derivatives of image
    .   points with respect to components of the rotation vector, translation vector, focal lengths,
    .   coordinates of the principal point and the distortion coefficients. In the old interface different
    .   components of the jacobian are returned via different output parameters.
    .   @param aspectRatio Optional "fixed aspect ratio" parameter. If the parameter is not 0, the
    .   function assumes that the aspect ratio (*fx/fy*) is fixed and correspondingly adjusts the jacobian
    .   matrix.
    .   
    .   The function computes projections of 3D points to the image plane given intrinsic and extrinsic
    .   camera parameters. Optionally, the function computes Jacobians - matrices of partial derivatives of
    .   image points coordinates (as functions of all the input parameters) with respect to the particular
    .   parameters, intrinsic and/or extrinsic. The Jacobians are used during the global optimization in
    .   calibrateCamera, solvePnP, and stereoCalibrate . The function itself can also be used to compute a
    .   re-projection error given the current intrinsic and extrinsic parameters.
    .   
    .   @note By setting rvec=tvec=(0,0,0) or by setting cameraMatrix to a 3x3 identity matrix, or by
    .   passing zero distortion coefficients, you can get various useful partial cases of the function. This
    .   means that you can compute the distorted coordinates for a sparse set of points or apply a
    .   perspective transformation (and also compute the derivatives) in the ideal zero-distortion setup.
    """
    pass

def PSNR(src1, src2): # real signature unknown; restored from __doc__
    """
    PSNR(src1, src2) -> retval
    .   @brief computes PSNR image/video quality metric
    .   
    .   see http://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio for details
    .   @todo document
    """
    pass

def putText(img, text, org, fontFace, fontScale, color, thickness=None, lineType=None, bottomLeftOrigin=None): # real signature unknown; restored from __doc__
    """
    putText(img, text, org, fontFace, fontScale, color[, thickness[, lineType[, bottomLeftOrigin]]]) -> img
    .   @brief Draws a text string.
    .   
    .   The function putText renders the specified text string in the image. Symbols that cannot be rendered
    .   using the specified font are replaced by question marks. See getTextSize for a text rendering code
    .   example.
    .   
    .   @param img Image.
    .   @param text Text string to be drawn.
    .   @param org Bottom-left corner of the text string in the image.
    .   @param fontFace Font type, see cv::HersheyFonts.
    .   @param fontScale Font scale factor that is multiplied by the font-specific base size.
    .   @param color Text color.
    .   @param thickness Thickness of the lines used to draw a text.
    .   @param lineType Line type. See the line for details.
    .   @param bottomLeftOrigin When true, the image data origin is at the bottom-left corner. Otherwise,
    .   it is at the top-left corner.
    """
    pass

def pyrDown(src, dst=None, dstsize=None, borderType=None): # real signature unknown; restored from __doc__
    """
    pyrDown(src[, dst[, dstsize[, borderType]]]) -> dst
    .   @brief Blurs an image and downsamples it.
    .   
    .   By default, size of the output image is computed as `Size((src.cols+1)/2, (src.rows+1)/2)`, but in
    .   any case, the following conditions should be satisfied:
    .   
    .   \f[\begin{array}{l} | \texttt{dstsize.width} *2-src.cols| \leq 2 \\ | \texttt{dstsize.height} *2-src.rows| \leq 2 \end{array}\f]
    .   
    .   The function performs the downsampling step of the Gaussian pyramid construction. First, it
    .   convolves the source image with the kernel:
    .   
    .   \f[\frac{1}{256} \begin{bmatrix} 1 & 4 & 6 & 4 & 1  \\ 4 & 16 & 24 & 16 & 4  \\ 6 & 24 & 36 & 24 & 6  \\ 4 & 16 & 24 & 16 & 4  \\ 1 & 4 & 6 & 4 & 1 \end{bmatrix}\f]
    .   
    .   Then, it downsamples the image by rejecting even rows and columns.
    .   
    .   @param src input image.
    .   @param dst output image; it has the specified size and the same type as src.
    .   @param dstsize size of the output image.
    .   @param borderType Pixel extrapolation method, see cv::BorderTypes (BORDER_CONSTANT isn't supported)
    """
    pass

def pyrMeanShiftFiltering(src, sp, sr, dst=None, maxLevel=None, termcrit=None): # real signature unknown; restored from __doc__
    """
    pyrMeanShiftFiltering(src, sp, sr[, dst[, maxLevel[, termcrit]]]) -> dst
    .   @brief Performs initial step of meanshift segmentation of an image.
    .   
    .   The function implements the filtering stage of meanshift segmentation, that is, the output of the
    .   function is the filtered "posterized" image with color gradients and fine-grain texture flattened.
    .   At every pixel (X,Y) of the input image (or down-sized input image, see below) the function executes
    .   meanshift iterations, that is, the pixel (X,Y) neighborhood in the joint space-color hyperspace is
    .   considered:
    .   
    .   \f[(x,y): X- \texttt{sp} \le x  \le X+ \texttt{sp} , Y- \texttt{sp} \le y  \le Y+ \texttt{sp} , ||(R,G,B)-(r,g,b)||   \le \texttt{sr}\f]
    .   
    .   where (R,G,B) and (r,g,b) are the vectors of color components at (X,Y) and (x,y), respectively
    .   (though, the algorithm does not depend on the color space used, so any 3-component color space can
    .   be used instead). Over the neighborhood the average spatial value (X',Y') and average color vector
    .   (R',G',B') are found and they act as the neighborhood center on the next iteration:
    .   
    .   \f[(X,Y)~(X',Y'), (R,G,B)~(R',G',B').\f]
    .   
    .   After the iterations over, the color components of the initial pixel (that is, the pixel from where
    .   the iterations started) are set to the final value (average color at the last iteration):
    .   
    .   \f[I(X,Y) <- (R*,G*,B*)\f]
    .   
    .   When maxLevel \> 0, the gaussian pyramid of maxLevel+1 levels is built, and the above procedure is
    .   run on the smallest layer first. After that, the results are propagated to the larger layer and the
    .   iterations are run again only on those pixels where the layer colors differ by more than sr from the
    .   lower-resolution layer of the pyramid. That makes boundaries of color regions sharper. Note that the
    .   results will be actually different from the ones obtained by running the meanshift procedure on the
    .   whole original image (i.e. when maxLevel==0).
    .   
    .   @param src The source 8-bit, 3-channel image.
    .   @param dst The destination image of the same format and the same size as the source.
    .   @param sp The spatial window radius.
    .   @param sr The color window radius.
    .   @param maxLevel Maximum level of the pyramid for the segmentation.
    .   @param termcrit Termination criteria: when to stop meanshift iterations.
    """
    pass

def pyrUp(src, dst=None, dstsize=None, borderType=None): # real signature unknown; restored from __doc__
    """
    pyrUp(src[, dst[, dstsize[, borderType]]]) -> dst
    .   @brief Upsamples an image and then blurs it.
    .   
    .   By default, size of the output image is computed as `Size(src.cols\*2, (src.rows\*2)`, but in any
    .   case, the following conditions should be satisfied:
    .   
    .   \f[\begin{array}{l} | \texttt{dstsize.width} -src.cols*2| \leq  ( \texttt{dstsize.width}   \mod  2)  \\ | \texttt{dstsize.height} -src.rows*2| \leq  ( \texttt{dstsize.height}   \mod  2) \end{array}\f]
    .   
    .   The function performs the upsampling step of the Gaussian pyramid construction, though it can
    .   actually be used to construct the Laplacian pyramid. First, it upsamples the source image by
    .   injecting even zero rows and columns and then convolves the result with the same kernel as in
    .   pyrDown multiplied by 4.
    .   
    .   @param src input image.
    .   @param dst output image. It has the specified size and the same type as src .
    .   @param dstsize size of the output image.
    .   @param borderType Pixel extrapolation method, see cv::BorderTypes (only BORDER_DEFAULT is supported)
    """
    pass

def randn(dst, mean, stddev): # real signature unknown; restored from __doc__
    """
    randn(dst, mean, stddev) -> dst
    .   @brief Fills the array with normally distributed random numbers.
    .   
    .   The function cv::randn fills the matrix dst with normally distributed random numbers with the specified
    .   mean vector and the standard deviation matrix. The generated random numbers are clipped to fit the
    .   value range of the output array data type.
    .   @param dst output array of random numbers; the array must be pre-allocated and have 1 to 4 channels.
    .   @param mean mean value (expectation) of the generated random numbers.
    .   @param stddev standard deviation of the generated random numbers; it can be either a vector (in
    .   which case a diagonal standard deviation matrix is assumed) or a square matrix.
    .   @sa RNG, randu
    """
    pass

def randShuffle(dst, iterFactor=None): # real signature unknown; restored from __doc__
    """
    randShuffle(dst[, iterFactor]) -> dst
    .   @brief Shuffles the array elements randomly.
    .   
    .   The function cv::randShuffle shuffles the specified 1D array by randomly choosing pairs of elements and
    .   swapping them. The number of such swap operations will be dst.rows\*dst.cols\*iterFactor .
    .   @param dst input/output numerical 1D array.
    .   @param iterFactor scale factor that determines the number of random swap operations (see the details
    .   below).
    .   @param rng optional random number generator used for shuffling; if it is zero, theRNG () is used
    .   instead.
    .   @sa RNG, sort
    """
    pass

def randu(dst, low, high): # real signature unknown; restored from __doc__
    """
    randu(dst, low, high) -> dst
    .   @brief Generates a single uniformly-distributed random number or an array of random numbers.
    .   
    .   Non-template variant of the function fills the matrix dst with uniformly-distributed
    .   random numbers from the specified range:
    .   \f[\texttt{low} _c  \leq \texttt{dst} (I)_c <  \texttt{high} _c\f]
    .   @param dst output array of random numbers; the array must be pre-allocated.
    .   @param low inclusive lower boundary of the generated random numbers.
    .   @param high exclusive upper boundary of the generated random numbers.
    .   @sa RNG, randn, theRNG
    """
    pass

def recoverPose(E, points1, points2, cameraMatrix, R=None, t=None, mask=None): # real signature unknown; restored from __doc__
    """
    recoverPose(E, points1, points2, cameraMatrix[, R[, t[, mask]]]) -> retval, R, t, mask
    .   @brief Recover relative camera rotation and translation from an estimated essential matrix and the
    .   corresponding points in two images, using cheirality check. Returns the number of inliers which pass
    .   the check.
    .   
    .   @param E The input essential matrix.
    .   @param points1 Array of N 2D points from the first image. The point coordinates should be
    .   floating-point (single or double precision).
    .   @param points2 Array of the second image points of the same size and format as points1 .
    .   @param cameraMatrix Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ .
    .   Note that this function assumes that points1 and points2 are feature points from cameras with the
    .   same camera matrix.
    .   @param R Recovered relative rotation.
    .   @param t Recoverd relative translation.
    .   @param mask Input/output mask for inliers in points1 and points2.
    .   :   If it is not empty, then it marks inliers in points1 and points2 for then given essential
    .   matrix E. Only these inliers will be used to recover pose. In the output mask only inliers
    .   which pass the cheirality check.
    .   This function decomposes an essential matrix using decomposeEssentialMat and then verifies possible
    .   pose hypotheses by doing cheirality check. The cheirality check basically means that the
    .   triangulated 3D points should have positive depth. Some details can be found in @cite Nister03 .
    .   
    .   This function can be used to process output E and mask from findEssentialMat. In this scenario,
    .   points1 and points2 are the same input for findEssentialMat. :
    .   @code
    .   // Example. Estimation of fundamental matrix using the RANSAC algorithm
    .   int point_count = 100;
    .   vector<Point2f> points1(point_count);
    .   vector<Point2f> points2(point_count);
    .   
    .   // initialize the points here ...
    .   for( int i = 0; i < point_count; i++ )
    .   {
    .   points1[i] = ...;
    .   points2[i] = ...;
    .   }
    .   
    .   // cametra matrix with both focal lengths = 1, and principal point = (0, 0)
    .   Mat cameraMatrix = Mat::eye(3, 3, CV_64F);
    .   
    .   Mat E, R, t, mask;
    .   
    .   E = findEssentialMat(points1, points2, cameraMatrix, RANSAC, 0.999, 1.0, mask);
    .   recoverPose(E, points1, points2, cameraMatrix, R, t, mask);
    .   @endcode
    
    
    
    recoverPose(E, points1, points2[, R[, t[, focal[, pp[, mask]]]]]) -> retval, R, t, mask
    .   @overload
    .   @param E The input essential matrix.
    .   @param points1 Array of N 2D points from the first image. The point coordinates should be
    .   floating-point (single or double precision).
    .   @param points2 Array of the second image points of the same size and format as points1 .
    .   @param R Recovered relative rotation.
    .   @param t Recoverd relative translation.
    .   @param focal Focal length of the camera. Note that this function assumes that points1 and points2
    .   are feature points from cameras with same focal length and principal point.
    .   @param pp principal point of the camera.
    .   @param mask Input/output mask for inliers in points1 and points2.
    .   :   If it is not empty, then it marks inliers in points1 and points2 for then given essential
    .   matrix E. Only these inliers will be used to recover pose. In the output mask only inliers
    .   which pass the cheirality check.
    .   
    .   This function differs from the one above that it computes camera matrix from focal length and
    .   principal point:
    .   
    .   \f[K =
    .   \begin{bmatrix}
    .   f & 0 & x_{pp}  \\
    .   0 & f & y_{pp}  \\
    .   0 & 0 & 1
    .   \end{bmatrix}\f]
    
    
    
    recoverPose(E, points1, points2, cameraMatrix, distanceThresh[, R[, t[, mask[, triangulatedPoints]]]]) -> retval, R, t, mask, triangulatedPoints
    .   @overload
    .   @param E The input essential matrix.
    .   @param points1 Array of N 2D points from the first image. The point coordinates should be
    .   floating-point (single or double precision).
    .   @param points2 Array of the second image points of the same size and format as points1.
    .   @param cameraMatrix Camera matrix \f$K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ .
    .   Note that this function assumes that points1 and points2 are feature points from cameras with the
    .   same camera matrix.
    .   @param R Recovered relative rotation.
    .   @param t Recoverd relative translation.
    .   @param distanceThresh threshold distance which is used to filter out far away points (i.e. infinite points).
    .   @param mask Input/output mask for inliers in points1 and points2.
    .   :   If it is not empty, then it marks inliers in points1 and points2 for then given essential
    .   matrix E. Only these inliers will be used to recover pose. In the output mask only inliers
    .   which pass the cheirality check.
    .   @param triangulatedPoints 3d points which were reconstructed by triangulation.
    """
    pass

def rectangle(img, pt1, pt2, color, thickness=None, lineType=None, shift=None): # real signature unknown; restored from __doc__
    """
    rectangle(img, pt1, pt2, color[, thickness[, lineType[, shift]]]) -> img
    .   @brief Draws a simple, thick, or filled up-right rectangle.
    .   
    .   The function rectangle draws a rectangle outline or a filled rectangle whose two opposite corners
    .   are pt1 and pt2.
    .   
    .   @param img Image.
    .   @param pt1 Vertex of the rectangle.
    .   @param pt2 Vertex of the rectangle opposite to pt1 .
    .   @param color Rectangle color or brightness (grayscale image).
    .   @param thickness Thickness of lines that make up the rectangle. Negative values, like CV_FILLED ,
    .   mean that the function has to draw a filled rectangle.
    .   @param lineType Type of the line. See the line description.
    .   @param shift Number of fractional bits in the point coordinates.
    """
    pass

def rectify3Collinear(cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, cameraMatrix3, distCoeffs3, imgpt1, imgpt3, imageSize, R12, T12, R13, T13, alpha, newImgSize, flags, R1=None, R2=None, R3=None, P1=None, P2=None, P3=None, Q=None): # real signature unknown; restored from __doc__
    """
    rectify3Collinear(cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, cameraMatrix3, distCoeffs3, imgpt1, imgpt3, imageSize, R12, T12, R13, T13, alpha, newImgSize, flags[, R1[, R2[, R3[, P1[, P2[, P3[, Q]]]]]]]) -> retval, R1, R2, R3, P1, P2, P3, Q, roi1, roi2
    .
    """
    pass

def reduce(src, dim, rtype, dst=None, dtype=None): # real signature unknown; restored from __doc__
    """
    reduce(src, dim, rtype[, dst[, dtype]]) -> dst
    .   @brief Reduces a matrix to a vector.
    .   
    .   The function cv::reduce reduces the matrix to a vector by treating the matrix rows/columns as a set of
    .   1D vectors and performing the specified operation on the vectors until a single row/column is
    .   obtained. For example, the function can be used to compute horizontal and vertical projections of a
    .   raster image. In case of REDUCE_MAX and REDUCE_MIN , the output image should have the same type as the source one.
    .   In case of REDUCE_SUM and REDUCE_AVG , the output may have a larger element bit-depth to preserve accuracy.
    .   And multi-channel arrays are also supported in these two reduction modes.
    .   @param src input 2D matrix.
    .   @param dst output vector. Its size and type is defined by dim and dtype parameters.
    .   @param dim dimension index along which the matrix is reduced. 0 means that the matrix is reduced to
    .   a single row. 1 means that the matrix is reduced to a single column.
    .   @param rtype reduction operation that could be one of cv::ReduceTypes
    .   @param dtype when negative, the output vector will have the same type as the input matrix,
    .   otherwise, its type will be CV_MAKE_TYPE(CV_MAT_DEPTH(dtype), src.channels()).
    .   @sa repeat
    """
    pass

def remap(src, map1, map2, interpolation, dst=None, borderMode=None, borderValue=None): # real signature unknown; restored from __doc__
    """
    remap(src, map1, map2, interpolation[, dst[, borderMode[, borderValue]]]) -> dst
    .   @brief Applies a generic geometrical transformation to an image.
    .   
    .   The function remap transforms the source image using the specified map:
    .   
    .   \f[\texttt{dst} (x,y) =  \texttt{src} (map_x(x,y),map_y(x,y))\f]
    .   
    .   where values of pixels with non-integer coordinates are computed using one of available
    .   interpolation methods. \f$map_x\f$ and \f$map_y\f$ can be encoded as separate floating-point maps
    .   in \f$map_1\f$ and \f$map_2\f$ respectively, or interleaved floating-point maps of \f$(x,y)\f$ in
    .   \f$map_1\f$, or fixed-point maps created by using convertMaps. The reason you might want to
    .   convert from floating to fixed-point representations of a map is that they can yield much faster
    .   (\~2x) remapping operations. In the converted case, \f$map_1\f$ contains pairs (cvFloor(x),
    .   cvFloor(y)) and \f$map_2\f$ contains indices in a table of interpolation coefficients.
    .   
    .   This function cannot operate in-place.
    .   
    .   @param src Source image.
    .   @param dst Destination image. It has the same size as map1 and the same type as src .
    .   @param map1 The first map of either (x,y) points or just x values having the type CV_16SC2 ,
    .   CV_32FC1, or CV_32FC2. See convertMaps for details on converting a floating point
    .   representation to fixed-point for speed.
    .   @param map2 The second map of y values having the type CV_16UC1, CV_32FC1, or none (empty map
    .   if map1 is (x,y) points), respectively.
    .   @param interpolation Interpolation method (see cv::InterpolationFlags). The method INTER_AREA is
    .   not supported by this function.
    .   @param borderMode Pixel extrapolation method (see cv::BorderTypes). When
    .   borderMode=BORDER_TRANSPARENT, it means that the pixels in the destination image that
    .   corresponds to the "outliers" in the source image are not modified by the function.
    .   @param borderValue Value used in case of a constant border. By default, it is 0.
    .   @note
    .   Due to current implementaion limitations the size of an input and output images should be less than 32767x32767.
    """
    pass

def repeat(src, ny, nx, dst=None): # real signature unknown; restored from __doc__
    """
    repeat(src, ny, nx[, dst]) -> dst
    .   @brief Fills the output array with repeated copies of the input array.
    .   
    .   The function cv::repeat duplicates the input array one or more times along each of the two axes:
    .   \f[\texttt{dst} _{ij}= \texttt{src} _{i\mod src.rows, \; j\mod src.cols }\f]
    .   The second variant of the function is more convenient to use with @ref MatrixExpressions.
    .   @param src input array to replicate.
    .   @param ny Flag to specify how many times the `src` is repeated along the
    .   vertical axis.
    .   @param nx Flag to specify how many times the `src` is repeated along the
    .   horizontal axis.
    .   @param dst output array of the same type as `src`.
    .   @sa cv::reduce
    """
    pass

def reprojectImageTo3D(disparity, Q, _3dImage=None, handleMissingValues=None, ddepth=None): # real signature unknown; restored from __doc__
    """
    reprojectImageTo3D(disparity, Q[, _3dImage[, handleMissingValues[, ddepth]]]) -> _3dImage
    .   @brief Reprojects a disparity image to 3D space.
    .   
    .   @param disparity Input single-channel 8-bit unsigned, 16-bit signed, 32-bit signed or 32-bit
    .   floating-point disparity image. If 16-bit signed format is used, the values are assumed to have no
    .   fractional bits.
    .   @param _3dImage Output 3-channel floating-point image of the same size as disparity . Each
    .   element of _3dImage(x,y) contains 3D coordinates of the point (x,y) computed from the disparity
    .   map.
    .   @param Q \f$4 \times 4\f$ perspective transformation matrix that can be obtained with stereoRectify.
    .   @param handleMissingValues Indicates, whether the function should handle missing values (i.e.
    .   points where the disparity was not computed). If handleMissingValues=true, then pixels with the
    .   minimal disparity that corresponds to the outliers (see StereoMatcher::compute ) are transformed
    .   to 3D points with a very large Z value (currently set to 10000).
    .   @param ddepth The optional output array depth. If it is -1, the output image will have CV_32F
    .   depth. ddepth can also be set to CV_16S, CV_32S or CV_32F.
    .   
    .   The function transforms a single-channel disparity map to a 3-channel image representing a 3D
    .   surface. That is, for each pixel (x,y) andthe corresponding disparity d=disparity(x,y) , it
    .   computes:
    .   
    .   \f[\begin{array}{l} [X \; Y \; Z \; W]^T =  \texttt{Q} *[x \; y \; \texttt{disparity} (x,y) \; 1]^T  \\ \texttt{\_3dImage} (x,y) = (X/W, \; Y/W, \; Z/W) \end{array}\f]
    .   
    .   The matrix Q can be an arbitrary \f$4 \times 4\f$ matrix (for example, the one computed by
    .   stereoRectify). To reproject a sparse set of points {(x,y,d),...} to 3D space, use
    .   perspectiveTransform .
    """
    pass

def resize(src, dsize, dst=None, fx=None, fy=None, interpolation=None): # real signature unknown; restored from __doc__
    """
    resize(src, dsize[, dst[, fx[, fy[, interpolation]]]]) -> dst
    .   @brief Resizes an image.
    .   
    .   The function resize resizes the image src down to or up to the specified size. Note that the
    .   initial dst type or size are not taken into account. Instead, the size and type are derived from
    .   the `src`,`dsize`,`fx`, and `fy`. If you want to resize src so that it fits the pre-created dst,
    .   you may call the function as follows:
    .   @code
    .   // explicitly specify dsize=dst.size(); fx and fy will be computed from that.
    .   resize(src, dst, dst.size(), 0, 0, interpolation);
    .   @endcode
    .   If you want to decimate the image by factor of 2 in each direction, you can call the function this
    .   way:
    .   @code
    .   // specify fx and fy and let the function compute the destination image size.
    .   resize(src, dst, Size(), 0.5, 0.5, interpolation);
    .   @endcode
    .   To shrink an image, it will generally look best with cv::INTER_AREA interpolation, whereas to
    .   enlarge an image, it will generally look best with cv::INTER_CUBIC (slow) or cv::INTER_LINEAR
    .   (faster but still looks OK).
    .   
    .   @param src input image.
    .   @param dst output image; it has the size dsize (when it is non-zero) or the size computed from
    .   src.size(), fx, and fy; the type of dst is the same as of src.
    .   @param dsize output image size; if it equals zero, it is computed as:
    .   \f[\texttt{dsize = Size(round(fx*src.cols), round(fy*src.rows))}\f]
    .   Either dsize or both fx and fy must be non-zero.
    .   @param fx scale factor along the horizontal axis; when it equals 0, it is computed as
    .   \f[\texttt{(double)dsize.width/src.cols}\f]
    .   @param fy scale factor along the vertical axis; when it equals 0, it is computed as
    .   \f[\texttt{(double)dsize.height/src.rows}\f]
    .   @param interpolation interpolation method, see cv::InterpolationFlags
    .   
    .   @sa  warpAffine, warpPerspective, remap
    """
    pass

def resizeWindow(winname, width, height): # real signature unknown; restored from __doc__
    """
    resizeWindow(winname, width, height) -> None
    .   @brief Resizes window to the specified size
    .   
    .   @note
    .   
    .   -   The specified window size is for the image area. Toolbars are not counted.
    .   -   Only windows created without cv::WINDOW_AUTOSIZE flag can be resized.
    .   
    .   @param winname Window name.
    .   @param width The new window width.
    .   @param height The new window height.
    """
    pass

def Rodrigues(src, dst=None, jacobian=None): # real signature unknown; restored from __doc__
    """
    Rodrigues(src[, dst[, jacobian]]) -> dst, jacobian
    .   @brief Converts a rotation matrix to a rotation vector or vice versa.
    .   
    .   @param src Input rotation vector (3x1 or 1x3) or rotation matrix (3x3).
    .   @param dst Output rotation matrix (3x3) or rotation vector (3x1 or 1x3), respectively.
    .   @param jacobian Optional output Jacobian matrix, 3x9 or 9x3, which is a matrix of partial
    .   derivatives of the output array components with respect to the input array components.
    .   
    .   \f[\begin{array}{l} \theta \leftarrow norm(r) \\ r  \leftarrow r/ \theta \\ R =  \cos{\theta} I + (1- \cos{\theta} ) r r^T +  \sin{\theta} \vecthreethree{0}{-r_z}{r_y}{r_z}{0}{-r_x}{-r_y}{r_x}{0} \end{array}\f]
    .   
    .   Inverse transformation can be also done easily, since
    .   
    .   \f[\sin ( \theta ) \vecthreethree{0}{-r_z}{r_y}{r_z}{0}{-r_x}{-r_y}{r_x}{0} = \frac{R - R^T}{2}\f]
    .   
    .   A rotation vector is a convenient and most compact representation of a rotation matrix (since any
    .   rotation matrix has just 3 degrees of freedom). The representation is used in the global 3D geometry
    .   optimization procedures like calibrateCamera, stereoCalibrate, or solvePnP .
    """
    pass

def rotate(src, rotateCode, dst=None): # real signature unknown; restored from __doc__
    """
    rotate(src, rotateCode[, dst]) -> dst
    .   @brief Rotates a 2D array in multiples of 90 degrees.
    .   The function rotate rotates the array in one of three different ways:
    .   *   Rotate by 90 degrees clockwise (rotateCode = ROTATE_90).
    .   *   Rotate by 180 degrees clockwise (rotateCode = ROTATE_180).
    .   *   Rotate by 270 degrees clockwise (rotateCode = ROTATE_270).
    .   @param src input array.
    .   @param dst output array of the same type as src.  The size is the same with ROTATE_180,
    .   and the rows and cols are switched for ROTATE_90 and ROTATE_270.
    .   @param rotateCode an enum to specify how to rotate the array; see the enum RotateFlags
    .   @sa transpose , repeat , completeSymm, flip, RotateFlags
    """
    pass

def rotatedRectangleIntersection(rect1, rect2, intersectingRegion=None): # real signature unknown; restored from __doc__
    """
    rotatedRectangleIntersection(rect1, rect2[, intersectingRegion]) -> retval, intersectingRegion
    .   @brief Finds out if there is any intersection between two rotated rectangles.
    .   
    .   If there is then the vertices of the intersecting region are returned as well.
    .   
    .   Below are some examples of intersection configurations. The hatched pattern indicates the
    .   intersecting region and the red vertices are returned by the function.
    .   
    .   ![intersection examples](pics/intersection.png)
    .   
    .   @param rect1 First rectangle
    .   @param rect2 Second rectangle
    .   @param intersectingRegion The output array of the verticies of the intersecting region. It returns
    .   at most 8 vertices. Stored as std::vector\<cv::Point2f\> or cv::Mat as Mx1 of type CV_32FC2.
    .   @returns One of cv::RectanglesIntersectTypes
    """
    pass

def RQDecomp3x3(src, mtxR=None, mtxQ=None, Qx=None, Qy=None, Qz=None): # real signature unknown; restored from __doc__
    """
    RQDecomp3x3(src[, mtxR[, mtxQ[, Qx[, Qy[, Qz]]]]]) -> retval, mtxR, mtxQ, Qx, Qy, Qz
    .   @brief Computes an RQ decomposition of 3x3 matrices.
    .   
    .   @param src 3x3 input matrix.
    .   @param mtxR Output 3x3 upper-triangular matrix.
    .   @param mtxQ Output 3x3 orthogonal matrix.
    .   @param Qx Optional output 3x3 rotation matrix around x-axis.
    .   @param Qy Optional output 3x3 rotation matrix around y-axis.
    .   @param Qz Optional output 3x3 rotation matrix around z-axis.
    .   
    .   The function computes a RQ decomposition using the given rotations. This function is used in
    .   decomposeProjectionMatrix to decompose the left 3x3 submatrix of a projection matrix into a camera
    .   and a rotation matrix.
    .   
    .   It optionally returns three rotation matrices, one for each axis, and the three Euler angles in
    .   degrees (as the return value) that could be used in OpenGL. Note, there is always more than one
    .   sequence of rotations about the three principal axes that results in the same orientation of an
    .   object, eg. see @cite Slabaugh . Returned tree rotation matrices and corresponding three Euler angules
    .   are only one of the possible solutions.
    """
    pass

def sampsonDistance(pt1, pt2, F): # real signature unknown; restored from __doc__
    """
    sampsonDistance(pt1, pt2, F) -> retval
    .   @brief Calculates the Sampson Distance between two points.
    .   
    .   The function sampsonDistance calculates and returns the first order approximation of the geometric error as:
    .   \f[sd( \texttt{pt1} , \texttt{pt2} )= \frac{(\texttt{pt2}^t \cdot \texttt{F} \cdot \texttt{pt1})^2}{(\texttt{F} \cdot \texttt{pt1})(0) + (\texttt{F} \cdot \texttt{pt1})(1) + (\texttt{F}^t \cdot \texttt{pt2})(0) + (\texttt{F}^t \cdot \texttt{pt2})(1)}\f]
    .   The fundamental matrix may be calculated using the cv::findFundamentalMat function. See HZ 11.4.3 for details.
    .   @param pt1 first homogeneous 2d point
    .   @param pt2 second homogeneous 2d point
    .   @param F fundamental matrix
    """
    pass

def scaleAdd(src1, alpha, src2, dst=None): # real signature unknown; restored from __doc__
    """
    scaleAdd(src1, alpha, src2[, dst]) -> dst
    .   @brief Calculates the sum of a scaled array and another array.
    .   
    .   The function scaleAdd is one of the classical primitive linear algebra operations, known as DAXPY
    .   or SAXPY in [BLAS](http://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms). It calculates
    .   the sum of a scaled array and another array:
    .   \f[\texttt{dst} (I)= \texttt{scale} \cdot \texttt{src1} (I) +  \texttt{src2} (I)\f]
    .   The function can also be emulated with a matrix expression, for example:
    .   @code{.cpp}
    .   Mat A(3, 3, CV_64F);
    .   ...
    .   A.row(0) = A.row(1)*2 + A.row(2);
    .   @endcode
    .   @param src1 first input array.
    .   @param alpha scale factor for the first array.
    .   @param src2 second input array of the same size and type as src1.
    .   @param dst output array of the same size and type as src1.
    .   @sa add, addWeighted, subtract, Mat::dot, Mat::convertTo
    """
    pass

def Scharr(src, ddepth, dx, dy, dst=None, scale=None, delta=None, borderType=None): # real signature unknown; restored from __doc__
    """
    Scharr(src, ddepth, dx, dy[, dst[, scale[, delta[, borderType]]]]) -> dst
    .   @brief Calculates the first x- or y- image derivative using Scharr operator.
    .   
    .   The function computes the first x- or y- spatial image derivative using the Scharr operator. The
    .   call
    .   
    .   \f[\texttt{Scharr(src, dst, ddepth, dx, dy, scale, delta, borderType)}\f]
    .   
    .   is equivalent to
    .   
    .   \f[\texttt{Sobel(src, dst, ddepth, dx, dy, CV\_SCHARR, scale, delta, borderType)} .\f]
    .   
    .   @param src input image.
    .   @param dst output image of the same size and the same number of channels as src.
    .   @param ddepth output image depth, see @ref filter_depths "combinations"
    .   @param dx order of the derivative x.
    .   @param dy order of the derivative y.
    .   @param scale optional scale factor for the computed derivative values; by default, no scaling is
    .   applied (see getDerivKernels for details).
    .   @param delta optional delta value that is added to the results prior to storing them in dst.
    .   @param borderType pixel extrapolation method, see cv::BorderTypes
    .   @sa  cartToPolar
    """
    pass

def seamlessClone(src, dst, mask, p, flags, blend=None): # real signature unknown; restored from __doc__
    """
    seamlessClone(src, dst, mask, p, flags[, blend]) -> blend
    .   @brief Image editing tasks concern either global changes (color/intensity corrections, filters,
    .   deformations) or local changes concerned to a selection. Here we are interested in achieving local
    .   changes, ones that are restricted to a region manually selected (ROI), in a seamless and effortless
    .   manner. The extent of the changes ranges from slight distortions to complete replacement by novel
    .   content @cite PM03 .
    .   
    .   @param src Input 8-bit 3-channel image.
    .   @param dst Input 8-bit 3-channel image.
    .   @param mask Input 8-bit 1 or 3-channel image.
    .   @param p Point in dst image where object is placed.
    .   @param blend Output image with the same size and type as dst.
    .   @param flags Cloning method that could be one of the following:
    .   -   **NORMAL_CLONE** The power of the method is fully expressed when inserting objects with
    .   complex outlines into a new background
    .   -   **MIXED_CLONE** The classic method, color-based selection and alpha masking might be time
    .   consuming and often leaves an undesirable halo. Seamless cloning, even averaged with the
    .   original image, is not effective. Mixed seamless cloning based on a loose selection proves
    .   effective.
    .   -   **MONOCHROME_TRANSFER** Monochrome transfer allows the user to easily replace certain features of
    .   one object by alternative features.
    """
    pass

def selectROI(windowName, img, showCrosshair=None, fromCenter=None): # real signature unknown; restored from __doc__
    """
    selectROI(windowName, img[, showCrosshair[, fromCenter]]) -> retval
    .   @brief Selects ROI on the given image.
    .   Function creates a window and allows user to select a ROI using mouse.
    .   Controls: use `space` or `enter` to finish selection, use key `c` to cancel selection (function will return the zero cv::Rect).
    .   
    .   @param windowName name of the window where selection process will be shown.
    .   @param img image to select a ROI.
    .   @param showCrosshair if true crosshair of selection rectangle will be shown.
    .   @param fromCenter if true center of selection will match initial mouse position. In opposite case a corner of
    .   selection rectangle will correspont to the initial mouse position.
    .   @return selected ROI or empty rect if selection canceled.
    .   
    .   @note The function sets it's own mouse callback for specified window using cv::setMouseCallback(windowName, ...).
    .   After finish of work an empty callback will be set for the used window.
    
    
    
    selectROI(img[, showCrosshair[, fromCenter]]) -> retval
    .   @overload
    """
    pass

def selectROIs(windowName, img, showCrosshair=None, fromCenter=None): # real signature unknown; restored from __doc__
    """
    selectROIs(windowName, img[, showCrosshair[, fromCenter]]) -> boundingBoxes
    .   @brief Selects ROIs on the given image.
    .   Function creates a window and allows user to select a ROIs using mouse.
    .   Controls: use `space` or `enter` to finish current selection and start a new one,
    .   use `esc` to terminate multiple ROI selection process.
    .   
    .   @param windowName name of the window where selection process will be shown.
    .   @param img image to select a ROI.
    .   @param boundingBoxes selected ROIs.
    .   @param showCrosshair if true crosshair of selection rectangle will be shown.
    .   @param fromCenter if true center of selection will match initial mouse position. In opposite case a corner of
    .   selection rectangle will correspont to the initial mouse position.
    .   
    .   @note The function sets it's own mouse callback for specified window using cv::setMouseCallback(windowName, ...).
    .   After finish of work an empty callback will be set for the used window.
    """
    pass

def sepFilter2D(src, ddepth, kernelX, kernelY, dst=None, anchor=None, delta=None, borderType=None): # real signature unknown; restored from __doc__
    """
    sepFilter2D(src, ddepth, kernelX, kernelY[, dst[, anchor[, delta[, borderType]]]]) -> dst
    .   @brief Applies a separable linear filter to an image.
    .   
    .   The function applies a separable linear filter to the image. That is, first, every row of src is
    .   filtered with the 1D kernel kernelX. Then, every column of the result is filtered with the 1D
    .   kernel kernelY. The final result shifted by delta is stored in dst .
    .   
    .   @param src Source image.
    .   @param dst Destination image of the same size and the same number of channels as src .
    .   @param ddepth Destination image depth, see @ref filter_depths "combinations"
    .   @param kernelX Coefficients for filtering each row.
    .   @param kernelY Coefficients for filtering each column.
    .   @param anchor Anchor position within the kernel. The default value \f$(-1,-1)\f$ means that the anchor
    .   is at the kernel center.
    .   @param delta Value added to the filtered results before storing them.
    .   @param borderType Pixel extrapolation method, see cv::BorderTypes
    .   @sa  filter2D, Sobel, GaussianBlur, boxFilter, blur
    """
    pass

def setIdentity(mtx, s=None): # real signature unknown; restored from __doc__
    """
    setIdentity(mtx[, s]) -> mtx
    .   @brief Initializes a scaled identity matrix.
    .   
    .   The function cv::setIdentity initializes a scaled identity matrix:
    .   \f[\texttt{mtx} (i,j)= \fork{\texttt{value}}{ if \(i=j\)}{0}{otherwise}\f]
    .   
    .   The function can also be emulated using the matrix initializers and the
    .   matrix expressions:
    .   @code
    .   Mat A = Mat::eye(4, 3, CV_32F)*5;
    .   // A will be set to [[5, 0, 0], [0, 5, 0], [0, 0, 5], [0, 0, 0]]
    .   @endcode
    .   @param mtx matrix to initialize (not necessarily square).
    .   @param s value to assign to diagonal elements.
    .   @sa Mat::zeros, Mat::ones, Mat::setTo, Mat::operator=
    """
    pass

def setMouseCallback(windowName, onMouse, param=None): # real signature unknown; restored from __doc__
    """ setMouseCallback(windowName, onMouse [, param]) -> None """
    pass

def setNumThreads(nthreads): # real signature unknown; restored from __doc__
    """
    setNumThreads(nthreads) -> None
    .   @brief OpenCV will try to set the number of threads for the next parallel region.
    .   
    .   If threads == 0, OpenCV will disable threading optimizations and run all it's functions
    .   sequentially. Passing threads \< 0 will reset threads number to system default. This function must
    .   be called outside of parallel region.
    .   
    .   OpenCV will try to run it's functions with specified threads number, but some behaviour differs from
    .   framework:
    .   -   `TBB` - User-defined parallel constructions will run with the same threads number, if
    .   another does not specified. If later on user creates own scheduler, OpenCV will use it.
    .   -   `OpenMP` - No special defined behaviour.
    .   -   `Concurrency` - If threads == 1, OpenCV will disable threading optimizations and run it's
    .   functions sequentially.
    .   -   `GCD` - Supports only values \<= 0.
    .   -   `C=` - No special defined behaviour.
    .   @param nthreads Number of threads used by OpenCV.
    .   @sa getNumThreads, getThreadNum
    """
    pass

def setRNGSeed(seed): # real signature unknown; restored from __doc__
    """
    setRNGSeed(seed) -> None
    .   @brief Sets state of default random number generator.
    .   
    .   The function cv::setRNGSeed sets state of default random number generator to custom value.
    .   @param seed new state for default random number generator
    .   @sa RNG, randu, randn
    """
    pass

def setTrackbarMax(trackbarname, winname, maxval): # real signature unknown; restored from __doc__
    """
    setTrackbarMax(trackbarname, winname, maxval) -> None
    .   @brief Sets the trackbar maximum position.
    .   
    .   The function sets the maximum position of the specified trackbar in the specified window.
    .   
    .   @note
    .   
    .   [__Qt Backend Only__] winname can be empty (or NULL) if the trackbar is attached to the control
    .   panel.
    .   
    .   @param trackbarname Name of the trackbar.
    .   @param winname Name of the window that is the parent of trackbar.
    .   @param maxval New maximum position.
    """
    pass

def setTrackbarMin(trackbarname, winname, minval): # real signature unknown; restored from __doc__
    """
    setTrackbarMin(trackbarname, winname, minval) -> None
    .   @brief Sets the trackbar minimum position.
    .   
    .   The function sets the minimum position of the specified trackbar in the specified window.
    .   
    .   @note
    .   
    .   [__Qt Backend Only__] winname can be empty (or NULL) if the trackbar is attached to the control
    .   panel.
    .   
    .   @param trackbarname Name of the trackbar.
    .   @param winname Name of the window that is the parent of trackbar.
    .   @param minval New maximum position.
    """
    pass

def setTrackbarPos(trackbarname, winname, pos): # real signature unknown; restored from __doc__
    """
    setTrackbarPos(trackbarname, winname, pos) -> None
    .   @brief Sets the trackbar position.
    .   
    .   The function sets the position of the specified trackbar in the specified window.
    .   
    .   @note
    .   
    .   [__Qt Backend Only__] winname can be empty (or NULL) if the trackbar is attached to the control
    .   panel.
    .   
    .   @param trackbarname Name of the trackbar.
    .   @param winname Name of the window that is the parent of trackbar.
    .   @param pos New position.
    """
    pass

def setUseOpenVX(flag): # real signature unknown; restored from __doc__
    """
    setUseOpenVX(flag) -> None
    .
    """
    pass

def setUseOptimized(onoff): # real signature unknown; restored from __doc__
    """
    setUseOptimized(onoff) -> None
    .   @brief Enables or disables the optimized code.
    .   
    .   The function can be used to dynamically turn on and off optimized code (code that uses SSE2, AVX,
    .   and other instructions on the platforms that support it). It sets a global flag that is further
    .   checked by OpenCV functions. Since the flag is not checked in the inner OpenCV loops, it is only
    .   safe to call the function on the very top level in your application where you can be sure that no
    .   other OpenCV function is currently executed.
    .   
    .   By default, the optimized code is enabled unless you disable it in CMake. The current status can be
    .   retrieved using useOptimized.
    .   @param onoff The boolean flag specifying whether the optimized code should be used (onoff=true)
    .   or not (onoff=false).
    """
    pass

def setWindowProperty(winname, prop_id, prop_value): # real signature unknown; restored from __doc__
    """
    setWindowProperty(winname, prop_id, prop_value) -> None
    .   @brief Changes parameters of a window dynamically.
    .   
    .   The function setWindowProperty enables changing properties of a window.
    .   
    .   @param winname Name of the window.
    .   @param prop_id Window property to edit. The supported operation flags are: (cv::WindowPropertyFlags)
    .   @param prop_value New value of the window property. The supported flags are: (cv::WindowFlags)
    """
    pass

def setWindowTitle(winname, title): # real signature unknown; restored from __doc__
    """
    setWindowTitle(winname, title) -> None
    .   @brief Updates window title
    .   @param winname Name of the window.
    .   @param title New title.
    """
    pass

def SimpleBlobDetector_create(parameters=None): # real signature unknown; restored from __doc__
    """
    SimpleBlobDetector_create([, parameters]) -> retval
    .
    """
    pass

def SimpleBlobDetector_Params(): # real signature unknown; restored from __doc__
    """
    SimpleBlobDetector_Params() -> <SimpleBlobDetector_Params object>
    .
    """
    pass

def Sobel(src, ddepth, dx, dy, dst=None, ksize=None, scale=None, delta=None, borderType=None): # real signature unknown; restored from __doc__
    """
    Sobel(src, ddepth, dx, dy[, dst[, ksize[, scale[, delta[, borderType]]]]]) -> dst
    .   @brief Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator.
    .   
    .   In all cases except one, the \f$\texttt{ksize} \times \texttt{ksize}\f$ separable kernel is used to
    .   calculate the derivative. When \f$\texttt{ksize = 1}\f$, the \f$3 \times 1\f$ or \f$1 \times 3\f$
    .   kernel is used (that is, no Gaussian smoothing is done). `ksize = 1` can only be used for the first
    .   or the second x- or y- derivatives.
    .   
    .   There is also the special value `ksize = CV_SCHARR (-1)` that corresponds to the \f$3\times3\f$ Scharr
    .   filter that may give more accurate results than the \f$3\times3\f$ Sobel. The Scharr aperture is
    .   
    .   \f[\vecthreethree{-3}{0}{3}{-10}{0}{10}{-3}{0}{3}\f]
    .   
    .   for the x-derivative, or transposed for the y-derivative.
    .   
    .   The function calculates an image derivative by convolving the image with the appropriate kernel:
    .   
    .   \f[\texttt{dst} =  \frac{\partial^{xorder+yorder} \texttt{src}}{\partial x^{xorder} \partial y^{yorder}}\f]
    .   
    .   The Sobel operators combine Gaussian smoothing and differentiation, so the result is more or less
    .   resistant to the noise. Most often, the function is called with ( xorder = 1, yorder = 0, ksize = 3)
    .   or ( xorder = 0, yorder = 1, ksize = 3) to calculate the first x- or y- image derivative. The first
    .   case corresponds to a kernel of:
    .   
    .   \f[\vecthreethree{-1}{0}{1}{-2}{0}{2}{-1}{0}{1}\f]
    .   
    .   The second case corresponds to a kernel of:
    .   
    .   \f[\vecthreethree{-1}{-2}{-1}{0}{0}{0}{1}{2}{1}\f]
    .   
    .   @param src input image.
    .   @param dst output image of the same size and the same number of channels as src .
    .   @param ddepth output image depth, see @ref filter_depths "combinations"; in the case of
    .   8-bit input images it will result in truncated derivatives.
    .   @param dx order of the derivative x.
    .   @param dy order of the derivative y.
    .   @param ksize size of the extended Sobel kernel; it must be 1, 3, 5, or 7.
    .   @param scale optional scale factor for the computed derivative values; by default, no scaling is
    .   applied (see cv::getDerivKernels for details).
    .   @param delta optional delta value that is added to the results prior to storing them in dst.
    .   @param borderType pixel extrapolation method, see cv::BorderTypes
    .   @sa  Scharr, Laplacian, sepFilter2D, filter2D, GaussianBlur, cartToPolar
    """
    pass

def solve(src1, src2, dst=None, flags=None): # real signature unknown; restored from __doc__
    """
    solve(src1, src2[, dst[, flags]]) -> retval, dst
    .   @brief Solves one or more linear systems or least-squares problems.
    .   
    .   The function cv::solve solves a linear system or least-squares problem (the
    .   latter is possible with SVD or QR methods, or by specifying the flag
    .   DECOMP_NORMAL ):
    .   \f[\texttt{dst} =  \arg \min _X \| \texttt{src1} \cdot \texttt{X} -  \texttt{src2} \|\f]
    .   
    .   If DECOMP_LU or DECOMP_CHOLESKY method is used, the function returns 1
    .   if src1 (or \f$\texttt{src1}^T\texttt{src1}\f$ ) is non-singular. Otherwise,
    .   it returns 0. In the latter case, dst is not valid. Other methods find a
    .   pseudo-solution in case of a singular left-hand side part.
    .   
    .   @note If you want to find a unity-norm solution of an under-defined
    .   singular system \f$\texttt{src1}\cdot\texttt{dst}=0\f$ , the function solve
    .   will not do the work. Use SVD::solveZ instead.
    .   
    .   @param src1 input matrix on the left-hand side of the system.
    .   @param src2 input matrix on the right-hand side of the system.
    .   @param dst output solution.
    .   @param flags solution (matrix inversion) method (cv::DecompTypes)
    .   @sa invert, SVD, eigen
    """
    pass

def solveCubic(coeffs, roots=None): # real signature unknown; restored from __doc__
    """
    solveCubic(coeffs[, roots]) -> retval, roots
    .   @brief Finds the real roots of a cubic equation.
    .   
    .   The function solveCubic finds the real roots of a cubic equation:
    .   -   if coeffs is a 4-element vector:
    .   \f[\texttt{coeffs} [0] x^3 +  \texttt{coeffs} [1] x^2 +  \texttt{coeffs} [2] x +  \texttt{coeffs} [3] = 0\f]
    .   -   if coeffs is a 3-element vector:
    .   \f[x^3 +  \texttt{coeffs} [0] x^2 +  \texttt{coeffs} [1] x +  \texttt{coeffs} [2] = 0\f]
    .   
    .   The roots are stored in the roots array.
    .   @param coeffs equation coefficients, an array of 3 or 4 elements.
    .   @param roots output array of real roots that has 1 or 3 elements.
    """
    pass

def solveLP(Func, Constr, z): # real signature unknown; restored from __doc__
    """
    solveLP(Func, Constr, z) -> retval
    .   @brief Solve given (non-integer) linear programming problem using the Simplex Algorithm (Simplex Method).
    .   
    .   What we mean here by "linear programming problem" (or LP problem, for short) can be formulated as:
    .   
    .   \f[\mbox{Maximize } c\cdot x\\
    .   \mbox{Subject to:}\\
    .   Ax\leq b\\
    .   x\geq 0\f]
    .   
    .   Where \f$c\f$ is fixed `1`-by-`n` row-vector, \f$A\f$ is fixed `m`-by-`n` matrix, \f$b\f$ is fixed `m`-by-`1`
    .   column vector and \f$x\f$ is an arbitrary `n`-by-`1` column vector, which satisfies the constraints.
    .   
    .   Simplex algorithm is one of many algorithms that are designed to handle this sort of problems
    .   efficiently. Although it is not optimal in theoretical sense (there exist algorithms that can solve
    .   any problem written as above in polynomial time, while simplex method degenerates to exponential
    .   time for some special cases), it is well-studied, easy to implement and is shown to work well for
    .   real-life purposes.
    .   
    .   The particular implementation is taken almost verbatim from **Introduction to Algorithms, third
    .   edition** by T. H. Cormen, C. E. Leiserson, R. L. Rivest and Clifford Stein. In particular, the
    .   Bland's rule <http://en.wikipedia.org/wiki/Bland%27s_rule> is used to prevent cycling.
    .   
    .   @param Func This row-vector corresponds to \f$c\f$ in the LP problem formulation (see above). It should
    .   contain 32- or 64-bit floating point numbers. As a convenience, column-vector may be also submitted,
    .   in the latter case it is understood to correspond to \f$c^T\f$.
    .   @param Constr `m`-by-`n+1` matrix, whose rightmost column corresponds to \f$b\f$ in formulation above
    .   and the remaining to \f$A\f$. It should containt 32- or 64-bit floating point numbers.
    .   @param z The solution will be returned here as a column-vector - it corresponds to \f$c\f$ in the
    .   formulation above. It will contain 64-bit floating point numbers.
    .   @return One of cv::SolveLPResult
    """
    pass

def solveP3P(objectPoints, imagePoints, cameraMatrix, distCoeffs, flags, rvecs=None, tvecs=None): # real signature unknown; restored from __doc__
    """
    solveP3P(objectPoints, imagePoints, cameraMatrix, distCoeffs, flags[, rvecs[, tvecs]]) -> retval, rvecs, tvecs
    .   @brief Finds an object pose from 3 3D-2D point correspondences.
    .   
    .   @param objectPoints Array of object points in the object coordinate space, 3x3 1-channel or
    .   1x3/3x1 3-channel. vector\<Point3f\> can be also passed here.
    .   @param imagePoints Array of corresponding image points, 3x2 1-channel or 1x3/3x1 2-channel.
    .   vector\<Point2f\> can be also passed here.
    .   @param cameraMatrix Input camera matrix \f$A = \vecthreethree{fx}{0}{cx}{0}{fy}{cy}{0}{0}{1}\f$ .
    .   @param distCoeffs Input vector of distortion coefficients
    .   \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ of
    .   4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are
    .   assumed.
    .   @param rvecs Output rotation vectors (see Rodrigues ) that, together with tvecs , brings points from
    .   the model coordinate system to the camera coordinate system. A P3P problem has up to 4 solutions.
    .   @param tvecs Output translation vectors.
    .   @param flags Method for solving a P3P problem:
    .   -   **SOLVEPNP_P3P** Method is based on the paper of X.S. Gao, X.-R. Hou, J. Tang, H.-F. Chang
    .   "Complete Solution Classification for the Perspective-Three-Point Problem".
    .   -   **SOLVEPNP_AP3P** Method is based on the paper of Tong Ke and Stergios I. Roumeliotis.
    .   "An Efficient Algebraic Solution to the Perspective-Three-Point Problem".
    .   
    .   The function estimates the object pose given 3 object points, their corresponding image
    .   projections, as well as the camera matrix and the distortion coefficients.
    """
    pass

def solvePnP(objectPoints, imagePoints, cameraMatrix, distCoeffs, rvec=None, tvec=None, useExtrinsicGuess=None, flags=None): # real signature unknown; restored from __doc__
    """
    solvePnP(objectPoints, imagePoints, cameraMatrix, distCoeffs[, rvec[, tvec[, useExtrinsicGuess[, flags]]]]) -> retval, rvec, tvec
    .   @brief Finds an object pose from 3D-2D point correspondences.
    .   
    .   @param objectPoints Array of object points in the object coordinate space, Nx3 1-channel or
    .   1xN/Nx1 3-channel, where N is the number of points. vector\<Point3f\> can be also passed here.
    .   @param imagePoints Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel,
    .   where N is the number of points. vector\<Point2f\> can be also passed here.
    .   @param cameraMatrix Input camera matrix \f$A = \vecthreethree{fx}{0}{cx}{0}{fy}{cy}{0}{0}{1}\f$ .
    .   @param distCoeffs Input vector of distortion coefficients
    .   \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ of
    .   4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are
    .   assumed.
    .   @param rvec Output rotation vector (see @ref Rodrigues ) that, together with tvec , brings points from
    .   the model coordinate system to the camera coordinate system.
    .   @param tvec Output translation vector.
    .   @param useExtrinsicGuess Parameter used for #SOLVEPNP_ITERATIVE. If true (1), the function uses
    .   the provided rvec and tvec values as initial approximations of the rotation and translation
    .   vectors, respectively, and further optimizes them.
    .   @param flags Method for solving a PnP problem:
    .   -   **SOLVEPNP_ITERATIVE** Iterative method is based on Levenberg-Marquardt optimization. In
    .   this case the function finds such a pose that minimizes reprojection error, that is the sum
    .   of squared distances between the observed projections imagePoints and the projected (using
    .   projectPoints ) objectPoints .
    .   -   **SOLVEPNP_P3P** Method is based on the paper of X.S. Gao, X.-R. Hou, J. Tang, H.-F. Chang
    .   "Complete Solution Classification for the Perspective-Three-Point Problem" (@cite gao2003complete).
    .   In this case the function requires exactly four object and image points.
    .   -   **SOLVEPNP_AP3P** Method is based on the paper of T. Ke, S. Roumeliotis
    .   "An Efficient Algebraic Solution to the Perspective-Three-Point Problem" (@cite Ke17).
    .   In this case the function requires exactly four object and image points.
    .   -   **SOLVEPNP_EPNP** Method has been introduced by F.Moreno-Noguer, V.Lepetit and P.Fua in the
    .   paper "EPnP: Efficient Perspective-n-Point Camera Pose Estimation" (@cite lepetit2009epnp).
    .   -   **SOLVEPNP_DLS** Method is based on the paper of Joel A. Hesch and Stergios I. Roumeliotis.
    .   "A Direct Least-Squares (DLS) Method for PnP" (@cite hesch2011direct).
    .   -   **SOLVEPNP_UPNP** Method is based on the paper of A.Penate-Sanchez, J.Andrade-Cetto,
    .   F.Moreno-Noguer. "Exhaustive Linearization for Robust Camera Pose and Focal Length
    .   Estimation" (@cite penate2013exhaustive). In this case the function also estimates the parameters \f$f_x\f$ and \f$f_y\f$
    .   assuming that both have the same value. Then the cameraMatrix is updated with the estimated
    .   focal length.
    .   -   **SOLVEPNP_AP3P** Method is based on the paper of Tong Ke and Stergios I. Roumeliotis.
    .   "An Efficient Algebraic Solution to the Perspective-Three-Point Problem". In this case the
    .   function requires exactly four object and image points.
    .   
    .   The function estimates the object pose given a set of object points, their corresponding image
    .   projections, as well as the camera matrix and the distortion coefficients.
    .   
    .   @note
    .   -   An example of how to use solvePnP for planar augmented reality can be found at
    .   opencv_source_code/samples/python/plane_ar.py
    .   -   If you are using Python:
    .   - Numpy array slices won't work as input because solvePnP requires contiguous
    .   arrays (enforced by the assertion using cv::Mat::checkVector() around line 55 of
    .   modules/calib3d/src/solvepnp.cpp version 2.4.9)
    .   - The P3P algorithm requires image points to be in an array of shape (N,1,2) due
    .   to its calling of cv::undistortPoints (around line 75 of modules/calib3d/src/solvepnp.cpp version 2.4.9)
    .   which requires 2-channel information.
    .   - Thus, given some data D = np.array(...) where D.shape = (N,M), in order to use a subset of
    .   it as, e.g., imagePoints, one must effectively copy it into a new array: imagePoints =
    .   np.ascontiguousarray(D[:,:2]).reshape((N,1,2))
    .   -   The methods **SOLVEPNP_DLS** and **SOLVEPNP_UPNP** cannot be used as the current implementations are
    .   unstable and sometimes give completly wrong results. If you pass one of these two
    .   flags, **SOLVEPNP_EPNP** method will be used instead.
    .   -   The minimum number of points is 4. In the case of **SOLVEPNP_P3P** and **SOLVEPNP_AP3P**
    .   methods, it is required to use exactly 4 points (the first 3 points are used to estimate all the solutions
    .   of the P3P problem, the last one is used to retain the best solution that minimizes the reprojection error).
    """
    pass

def solvePnPRansac(objectPoints, imagePoints, cameraMatrix, distCoeffs, rvec=None, tvec=None, useExtrinsicGuess=None, iterationsCount=None, reprojectionError=None, confidence=None, inliers=None, flags=None): # real signature unknown; restored from __doc__
    """
    solvePnPRansac(objectPoints, imagePoints, cameraMatrix, distCoeffs[, rvec[, tvec[, useExtrinsicGuess[, iterationsCount[, reprojectionError[, confidence[, inliers[, flags]]]]]]]]) -> retval, rvec, tvec, inliers
    .   @brief Finds an object pose from 3D-2D point correspondences using the RANSAC scheme.
    .   
    .   @param objectPoints Array of object points in the object coordinate space, Nx3 1-channel or
    .   1xN/Nx1 3-channel, where N is the number of points. vector\<Point3f\> can be also passed here.
    .   @param imagePoints Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel,
    .   where N is the number of points. vector\<Point2f\> can be also passed here.
    .   @param cameraMatrix Input camera matrix \f$A = \vecthreethree{fx}{0}{cx}{0}{fy}{cy}{0}{0}{1}\f$ .
    .   @param distCoeffs Input vector of distortion coefficients
    .   \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ of
    .   4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are
    .   assumed.
    .   @param rvec Output rotation vector (see Rodrigues ) that, together with tvec , brings points from
    .   the model coordinate system to the camera coordinate system.
    .   @param tvec Output translation vector.
    .   @param useExtrinsicGuess Parameter used for SOLVEPNP_ITERATIVE. If true (1), the function uses
    .   the provided rvec and tvec values as initial approximations of the rotation and translation
    .   vectors, respectively, and further optimizes them.
    .   @param iterationsCount Number of iterations.
    .   @param reprojectionError Inlier threshold value used by the RANSAC procedure. The parameter value
    .   is the maximum allowed distance between the observed and computed point projections to consider it
    .   an inlier.
    .   @param confidence The probability that the algorithm produces a useful result.
    .   @param inliers Output vector that contains indices of inliers in objectPoints and imagePoints .
    .   @param flags Method for solving a PnP problem (see solvePnP ).
    .   
    .   The function estimates an object pose given a set of object points, their corresponding image
    .   projections, as well as the camera matrix and the distortion coefficients. This function finds such
    .   a pose that minimizes reprojection error, that is, the sum of squared distances between the observed
    .   projections imagePoints and the projected (using projectPoints ) objectPoints. The use of RANSAC
    .   makes the function resistant to outliers.
    .   
    .   @note
    .   -   An example of how to use solvePNPRansac for object detection can be found at
    .   opencv_source_code/samples/cpp/tutorial_code/calib3d/real_time_pose_estimation/
    .   -   The default method used to estimate the camera pose for the Minimal Sample Sets step
    .   is #SOLVEPNP_EPNP. Exceptions are:
    .   - if you choose #SOLVEPNP_P3P or #SOLVEPNP_AP3P, these methods will be used.
    .   - if the number of input points is equal to 4, #SOLVEPNP_P3P is used.
    .   -   The method used to estimate the camera pose using all the inliers is defined by the
    .   flags parameters unless it is equal to #SOLVEPNP_P3P or #SOLVEPNP_AP3P. In this case,
    .   the method #SOLVEPNP_EPNP will be used instead.
    """
    pass

def solvePoly(coeffs, roots=None, maxIters=None): # real signature unknown; restored from __doc__
    """
    solvePoly(coeffs[, roots[, maxIters]]) -> retval, roots
    .   @brief Finds the real or complex roots of a polynomial equation.
    .   
    .   The function cv::solvePoly finds real and complex roots of a polynomial equation:
    .   \f[\texttt{coeffs} [n] x^{n} +  \texttt{coeffs} [n-1] x^{n-1} + ... +  \texttt{coeffs} [1] x +  \texttt{coeffs} [0] = 0\f]
    .   @param coeffs array of polynomial coefficients.
    .   @param roots output (complex) array of roots.
    .   @param maxIters maximum number of iterations the algorithm does.
    """
    pass

def sort(src, flags, dst=None): # real signature unknown; restored from __doc__
    """
    sort(src, flags[, dst]) -> dst
    .   @brief Sorts each row or each column of a matrix.
    .   
    .   The function cv::sort sorts each matrix row or each matrix column in
    .   ascending or descending order. So you should pass two operation flags to
    .   get desired behaviour. If you want to sort matrix rows or columns
    .   lexicographically, you can use STL std::sort generic function with the
    .   proper comparison predicate.
    .   
    .   @param src input single-channel array.
    .   @param dst output array of the same size and type as src.
    .   @param flags operation flags, a combination of cv::SortFlags
    .   @sa sortIdx, randShuffle
    """
    pass

def sortIdx(src, flags, dst=None): # real signature unknown; restored from __doc__
    """
    sortIdx(src, flags[, dst]) -> dst
    .   @brief Sorts each row or each column of a matrix.
    .   
    .   The function cv::sortIdx sorts each matrix row or each matrix column in the
    .   ascending or descending order. So you should pass two operation flags to
    .   get desired behaviour. Instead of reordering the elements themselves, it
    .   stores the indices of sorted elements in the output array. For example:
    .   @code
    .   Mat A = Mat::eye(3,3,CV_32F), B;
    .   sortIdx(A, B, SORT_EVERY_ROW + SORT_ASCENDING);
    .   // B will probably contain
    .   // (because of equal elements in A some permutations are possible):
    .   // [[1, 2, 0], [0, 2, 1], [0, 1, 2]]
    .   @endcode
    .   @param src input single-channel array.
    .   @param dst output integer array of the same size as src.
    .   @param flags operation flags that could be a combination of cv::SortFlags
    .   @sa sort, randShuffle
    """
    pass

def SparsePyrLKOpticalFlow_create(winSize=None, maxLevel=None, crit=None, flags=None, minEigThreshold=None): # real signature unknown; restored from __doc__
    """
    SparsePyrLKOpticalFlow_create([, winSize[, maxLevel[, crit[, flags[, minEigThreshold]]]]]) -> retval
    .
    """
    pass

def spatialGradient(src, dx=None, dy=None, ksize=None, borderType=None): # real signature unknown; restored from __doc__
    """
    spatialGradient(src[, dx[, dy[, ksize[, borderType]]]]) -> dx, dy
    .   @brief Calculates the first order image derivative in both x and y using a Sobel operator
    .   
    .   Equivalent to calling:
    .   
    .   @code
    .   Sobel( src, dx, CV_16SC1, 1, 0, 3 );
    .   Sobel( src, dy, CV_16SC1, 0, 1, 3 );
    .   @endcode
    .   
    .   @param src input image.
    .   @param dx output image with first-order derivative in x.
    .   @param dy output image with first-order derivative in y.
    .   @param ksize size of Sobel kernel. It must be 3.
    .   @param borderType pixel extrapolation method, see cv::BorderTypes
    .   
    .   @sa Sobel
    """
    pass

def split(m, mv=None): # real signature unknown; restored from __doc__
    """
    split(m[, mv]) -> mv
    .   @overload
    .   @param m input multi-channel array.
    .   @param mv output vector of arrays; the arrays themselves are reallocated, if needed.
    """
    pass

def sqrBoxFilter(_src, ddepth, ksize, _dst=None, anchor=None, normalize=None, borderType=None): # real signature unknown; restored from __doc__
    """
    sqrBoxFilter(_src, ddepth, ksize[, _dst[, anchor[, normalize[, borderType]]]]) -> _dst
    .   @brief Calculates the normalized sum of squares of the pixel values overlapping the filter.
    .   
    .   For every pixel \f$ (x, y) \f$ in the source image, the function calculates the sum of squares of those neighboring
    .   pixel values which overlap the filter placed over the pixel \f$ (x, y) \f$.
    .   
    .   The unnormalized square box filter can be useful in computing local image statistics such as the the local
    .   variance and standard deviation around the neighborhood of a pixel.
    .   
    .   @param _src input image
    .   @param _dst output image of the same size and type as _src
    .   @param ddepth the output image depth (-1 to use src.depth())
    .   @param ksize kernel size
    .   @param anchor kernel anchor point. The default value of Point(-1, -1) denotes that the anchor is at the kernel
    .   center.
    .   @param normalize flag, specifying whether the kernel is to be normalized by it's area or not.
    .   @param borderType border mode used to extrapolate pixels outside of the image, see cv::BorderTypes
    .   @sa boxFilter
    """
    pass

def sqrt(src, dst=None): # real signature unknown; restored from __doc__
    """
    sqrt(src[, dst]) -> dst
    .   @brief Calculates a square root of array elements.
    .   
    .   The function cv::sqrt calculates a square root of each input array element.
    .   In case of multi-channel arrays, each channel is processed
    .   independently. The accuracy is approximately the same as of the built-in
    .   std::sqrt .
    .   @param src input floating-point array.
    .   @param dst output array of the same size and type as src.
    """
    pass

def startWindowThread(): # real signature unknown; restored from __doc__
    """
    startWindowThread() -> retval
    .
    """
    pass

def StereoBM_create(numDisparities=None, blockSize=None): # real signature unknown; restored from __doc__
    """
    StereoBM_create([, numDisparities[, blockSize]]) -> retval
    .   @brief Creates StereoBM object
    .   
    .   @param numDisparities the disparity search range. For each pixel algorithm will find the best
    .   disparity from 0 (default minimum disparity) to numDisparities. The search range can then be
    .   shifted by changing the minimum disparity.
    .   @param blockSize the linear size of the blocks compared by the algorithm. The size should be odd
    .   (as the block is centered at the current pixel). Larger block size implies smoother, though less
    .   accurate disparity map. Smaller block size gives more detailed disparity map, but there is higher
    .   chance for algorithm to find a wrong correspondence.
    .   
    .   The function create StereoBM object. You can then call StereoBM::compute() to compute disparity for
    .   a specific stereo pair.
    """
    pass

def stereoCalibrate(objectPoints, imagePoints1, imagePoints2, cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, imageSize, R=None, T=None, E=None, F=None, flags=None, criteria=None): # real signature unknown; restored from __doc__
    """
    stereoCalibrate(objectPoints, imagePoints1, imagePoints2, cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, imageSize[, R[, T[, E[, F[, flags[, criteria]]]]]]) -> retval, cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, R, T, E, F
    .   @brief Calibrates the stereo camera.
    .   
    .   @param objectPoints Vector of vectors of the calibration pattern points.
    .   @param imagePoints1 Vector of vectors of the projections of the calibration pattern points,
    .   observed by the first camera.
    .   @param imagePoints2 Vector of vectors of the projections of the calibration pattern points,
    .   observed by the second camera.
    .   @param cameraMatrix1 Input/output first camera matrix:
    .   \f$\vecthreethree{f_x^{(j)}}{0}{c_x^{(j)}}{0}{f_y^{(j)}}{c_y^{(j)}}{0}{0}{1}\f$ , \f$j = 0,\, 1\f$ . If
    .   any of CALIB_USE_INTRINSIC_GUESS , CALIB_FIX_ASPECT_RATIO ,
    .   CALIB_FIX_INTRINSIC , or CALIB_FIX_FOCAL_LENGTH are specified, some or all of the
    .   matrix components must be initialized. See the flags description for details.
    .   @param distCoeffs1 Input/output vector of distortion coefficients
    .   \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6 [, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$ of
    .   4, 5, 8, 12 or 14 elements. The output vector length depends on the flags.
    .   @param cameraMatrix2 Input/output second camera matrix. The parameter is similar to cameraMatrix1
    .   @param distCoeffs2 Input/output lens distortion coefficients for the second camera. The parameter
    .   is similar to distCoeffs1 .
    .   @param imageSize Size of the image used only to initialize intrinsic camera matrix.
    .   @param R Output rotation matrix between the 1st and the 2nd camera coordinate systems.
    .   @param T Output translation vector between the coordinate systems of the cameras.
    .   @param E Output essential matrix.
    .   @param F Output fundamental matrix.
    .   @param flags Different flags that may be zero or a combination of the following values:
    .   -   **CALIB_FIX_INTRINSIC** Fix cameraMatrix? and distCoeffs? so that only R, T, E , and F
    .   matrices are estimated.
    .   -   **CALIB_USE_INTRINSIC_GUESS** Optimize some or all of the intrinsic parameters
    .   according to the specified flags. Initial values are provided by the user.
    .   -   **CALIB_FIX_PRINCIPAL_POINT** Fix the principal points during the optimization.
    .   -   **CALIB_FIX_FOCAL_LENGTH** Fix \f$f^{(j)}_x\f$ and \f$f^{(j)}_y\f$ .
    .   -   **CALIB_FIX_ASPECT_RATIO** Optimize \f$f^{(j)}_y\f$ . Fix the ratio \f$f^{(j)}_x/f^{(j)}_y\f$
    .   .
    .   -   **CALIB_SAME_FOCAL_LENGTH** Enforce \f$f^{(0)}_x=f^{(1)}_x\f$ and \f$f^{(0)}_y=f^{(1)}_y\f$ .
    .   -   **CALIB_ZERO_TANGENT_DIST** Set tangential distortion coefficients for each camera to
    .   zeros and fix there.
    .   -   **CALIB_FIX_K1,...,CALIB_FIX_K6** Do not change the corresponding radial
    .   distortion coefficient during the optimization. If CALIB_USE_INTRINSIC_GUESS is set,
    .   the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
    .   -   **CALIB_RATIONAL_MODEL** Enable coefficients k4, k5, and k6. To provide the backward
    .   compatibility, this extra flag should be explicitly specified to make the calibration
    .   function use the rational model and return 8 coefficients. If the flag is not set, the
    .   function computes and returns only 5 distortion coefficients.
    .   -   **CALIB_THIN_PRISM_MODEL** Coefficients s1, s2, s3 and s4 are enabled. To provide the
    .   backward compatibility, this extra flag should be explicitly specified to make the
    .   calibration function use the thin prism model and return 12 coefficients. If the flag is not
    .   set, the function computes and returns only 5 distortion coefficients.
    .   -   **CALIB_FIX_S1_S2_S3_S4** The thin prism distortion coefficients are not changed during
    .   the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the
    .   supplied distCoeffs matrix is used. Otherwise, it is set to 0.
    .   -   **CALIB_TILTED_MODEL** Coefficients tauX and tauY are enabled. To provide the
    .   backward compatibility, this extra flag should be explicitly specified to make the
    .   calibration function use the tilted sensor model and return 14 coefficients. If the flag is not
    .   set, the function computes and returns only 5 distortion coefficients.
    .   -   **CALIB_FIX_TAUX_TAUY** The coefficients of the tilted sensor model are not changed during
    .   the optimization. If CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the
    .   supplied distCoeffs matrix is used. Otherwise, it is set to 0.
    .   @param criteria Termination criteria for the iterative optimization algorithm.
    .   
    .   The function estimates transformation between two cameras making a stereo pair. If you have a stereo
    .   camera where the relative position and orientation of two cameras is fixed, and if you computed
    .   poses of an object relative to the first camera and to the second camera, (R1, T1) and (R2, T2),
    .   respectively (this can be done with solvePnP ), then those poses definitely relate to each other.
    .   This means that, given ( \f$R_1\f$,\f$T_1\f$ ), it should be possible to compute ( \f$R_2\f$,\f$T_2\f$ ). You only
    .   need to know the position and orientation of the second camera relative to the first camera. This is
    .   what the described function does. It computes ( \f$R\f$,\f$T\f$ ) so that:
    .   
    .   \f[R_2=R*R_1\f]
    .   \f[T_2=R*T_1 + T,\f]
    .   
    .   Optionally, it computes the essential matrix E:
    .   
    .   \f[E= \vecthreethree{0}{-T_2}{T_1}{T_2}{0}{-T_0}{-T_1}{T_0}{0} *R\f]
    .   
    .   where \f$T_i\f$ are components of the translation vector \f$T\f$ : \f$T=[T_0, T_1, T_2]^T\f$ . And the function
    .   can also compute the fundamental matrix F:
    .   
    .   \f[F = cameraMatrix2^{-T} E cameraMatrix1^{-1}\f]
    .   
    .   Besides the stereo-related information, the function can also perform a full calibration of each of
    .   two cameras. However, due to the high dimensionality of the parameter space and noise in the input
    .   data, the function can diverge from the correct solution. If the intrinsic parameters can be
    .   estimated with high accuracy for each of the cameras individually (for example, using
    .   calibrateCamera ), you are recommended to do so and then pass CALIB_FIX_INTRINSIC flag to the
    .   function along with the computed intrinsic parameters. Otherwise, if all the parameters are
    .   estimated at once, it makes sense to restrict some parameters, for example, pass
    .   CALIB_SAME_FOCAL_LENGTH and CALIB_ZERO_TANGENT_DIST flags, which is usually a
    .   reasonable assumption.
    .   
    .   Similarly to calibrateCamera , the function minimizes the total re-projection error for all the
    .   points in all the available views from both cameras. The function returns the final value of the
    .   re-projection error.
    """
    pass

def stereoRectify(cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, imageSize, R, T, R1=None, R2=None, P1=None, P2=None, Q=None, flags=None, alpha=None, newImageSize=None): # real signature unknown; restored from __doc__
    """
    stereoRectify(cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, imageSize, R, T[, R1[, R2[, P1[, P2[, Q[, flags[, alpha[, newImageSize]]]]]]]]) -> R1, R2, P1, P2, Q, validPixROI1, validPixROI2
    .   @brief Computes rectification transforms for each head of a calibrated stereo camera.
    .   
    .   @param cameraMatrix1 First camera matrix.
    .   @param distCoeffs1 First camera distortion parameters.
    .   @param cameraMatrix2 Second camera matrix.
    .   @param distCoeffs2 Second camera distortion parameters.
    .   @param imageSize Size of the image used for stereo calibration.
    .   @param R Rotation matrix between the coordinate systems of the first and the second cameras.
    .   @param T Translation vector between coordinate systems of the cameras.
    .   @param R1 Output 3x3 rectification transform (rotation matrix) for the first camera.
    .   @param R2 Output 3x3 rectification transform (rotation matrix) for the second camera.
    .   @param P1 Output 3x4 projection matrix in the new (rectified) coordinate systems for the first
    .   camera.
    .   @param P2 Output 3x4 projection matrix in the new (rectified) coordinate systems for the second
    .   camera.
    .   @param Q Output \f$4 \times 4\f$ disparity-to-depth mapping matrix (see reprojectImageTo3D ).
    .   @param flags Operation flags that may be zero or CALIB_ZERO_DISPARITY . If the flag is set,
    .   the function makes the principal points of each camera have the same pixel coordinates in the
    .   rectified views. And if the flag is not set, the function may still shift the images in the
    .   horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the
    .   useful image area.
    .   @param alpha Free scaling parameter. If it is -1 or absent, the function performs the default
    .   scaling. Otherwise, the parameter should be between 0 and 1. alpha=0 means that the rectified
    .   images are zoomed and shifted so that only valid pixels are visible (no black areas after
    .   rectification). alpha=1 means that the rectified image is decimated and shifted so that all the
    .   pixels from the original images from the cameras are retained in the rectified images (no source
    .   image pixels are lost). Obviously, any intermediate value yields an intermediate result between
    .   those two extreme cases.
    .   @param newImageSize New image resolution after rectification. The same size should be passed to
    .   initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0)
    .   is passed (default), it is set to the original imageSize . Setting it to larger value can help you
    .   preserve details in the original image, especially when there is a big radial distortion.
    .   @param validPixROI1 Optional output rectangles inside the rectified images where all the pixels
    .   are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller
    .   (see the picture below).
    .   @param validPixROI2 Optional output rectangles inside the rectified images where all the pixels
    .   are valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller
    .   (see the picture below).
    .   
    .   The function computes the rotation matrices for each camera that (virtually) make both camera image
    .   planes the same plane. Consequently, this makes all the epipolar lines parallel and thus simplifies
    .   the dense stereo correspondence problem. The function takes the matrices computed by stereoCalibrate
    .   as input. As output, it provides two rotation matrices and also two projection matrices in the new
    .   coordinates. The function distinguishes the following two cases:
    .   
    .   -   **Horizontal stereo**: the first and the second camera views are shifted relative to each other
    .   mainly along the x axis (with possible small vertical shift). In the rectified images, the
    .   corresponding epipolar lines in the left and right cameras are horizontal and have the same
    .   y-coordinate. P1 and P2 look like:
    .   
    .   \f[\texttt{P1} = \begin{bmatrix} f & 0 & cx_1 & 0 \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\f]
    .   
    .   \f[\texttt{P2} = \begin{bmatrix} f & 0 & cx_2 & T_x*f \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix} ,\f]
    .   
    .   where \f$T_x\f$ is a horizontal shift between the cameras and \f$cx_1=cx_2\f$ if
    .   CALIB_ZERO_DISPARITY is set.
    .   
    .   -   **Vertical stereo**: the first and the second camera views are shifted relative to each other
    .   mainly in vertical direction (and probably a bit in the horizontal direction too). The epipolar
    .   lines in the rectified images are vertical and have the same x-coordinate. P1 and P2 look like:
    .   
    .   \f[\texttt{P1} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_1 & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\f]
    .   
    .   \f[\texttt{P2} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_2 & T_y*f \\ 0 & 0 & 1 & 0 \end{bmatrix} ,\f]
    .   
    .   where \f$T_y\f$ is a vertical shift between the cameras and \f$cy_1=cy_2\f$ if CALIB_ZERO_DISPARITY is
    .   set.
    .   
    .   As you can see, the first three columns of P1 and P2 will effectively be the new "rectified" camera
    .   matrices. The matrices, together with R1 and R2 , can then be passed to initUndistortRectifyMap to
    .   initialize the rectification map for each camera.
    .   
    .   See below the screenshot from the stereo_calib.cpp sample. Some red horizontal lines pass through
    .   the corresponding image regions. This means that the images are well rectified, which is what most
    .   stereo correspondence algorithms rely on. The green rectangles are roi1 and roi2 . You see that
    .   their interiors are all valid pixels.
    .   
    .   ![image](pics/stereo_undistort.jpg)
    """
    pass

def stereoRectifyUncalibrated(points1, points2, F, imgSize, H1=None, H2=None, threshold=None): # real signature unknown; restored from __doc__
    """
    stereoRectifyUncalibrated(points1, points2, F, imgSize[, H1[, H2[, threshold]]]) -> retval, H1, H2
    .   @brief Computes a rectification transform for an uncalibrated stereo camera.
    .   
    .   @param points1 Array of feature points in the first image.
    .   @param points2 The corresponding points in the second image. The same formats as in
    .   findFundamentalMat are supported.
    .   @param F Input fundamental matrix. It can be computed from the same set of point pairs using
    .   findFundamentalMat .
    .   @param imgSize Size of the image.
    .   @param H1 Output rectification homography matrix for the first image.
    .   @param H2 Output rectification homography matrix for the second image.
    .   @param threshold Optional threshold used to filter out the outliers. If the parameter is greater
    .   than zero, all the point pairs that do not comply with the epipolar geometry (that is, the points
    .   for which \f$|\texttt{points2[i]}^T*\texttt{F}*\texttt{points1[i]}|>\texttt{threshold}\f$ ) are
    .   rejected prior to computing the homographies. Otherwise,all the points are considered inliers.
    .   
    .   The function computes the rectification transformations without knowing intrinsic parameters of the
    .   cameras and their relative position in the space, which explains the suffix "uncalibrated". Another
    .   related difference from stereoRectify is that the function outputs not the rectification
    .   transformations in the object (3D) space, but the planar perspective transformations encoded by the
    .   homography matrices H1 and H2 . The function implements the algorithm @cite Hartley99 .
    .   
    .   @note
    .   While the algorithm does not need to know the intrinsic parameters of the cameras, it heavily
    .   depends on the epipolar geometry. Therefore, if the camera lenses have a significant distortion,
    .   it would be better to correct it before computing the fundamental matrix and calling this
    .   function. For example, distortion coefficients can be estimated for each head of stereo camera
    .   separately by using calibrateCamera . Then, the images can be corrected using undistort , or
    .   just the point coordinates can be corrected with undistortPoints .
    """
    pass

def StereoSGBM_create(minDisparity=None, numDisparities=None, blockSize=None, P1=None, P2=None, disp12MaxDiff=None, preFilterCap=None, uniquenessRatio=None, speckleWindowSize=None, speckleRange=None, mode=None): # real signature unknown; restored from __doc__
    """
    StereoSGBM_create([, minDisparity[, numDisparities[, blockSize[, P1[, P2[, disp12MaxDiff[, preFilterCap[, uniquenessRatio[, speckleWindowSize[, speckleRange[, mode]]]]]]]]]]]) -> retval
    .   @brief Creates StereoSGBM object
    .   
    .   @param minDisparity Minimum possible disparity value. Normally, it is zero but sometimes
    .   rectification algorithms can shift images, so this parameter needs to be adjusted accordingly.
    .   @param numDisparities Maximum disparity minus minimum disparity. The value is always greater than
    .   zero. In the current implementation, this parameter must be divisible by 16.
    .   @param blockSize Matched block size. It must be an odd number \>=1 . Normally, it should be
    .   somewhere in the 3..11 range.
    .   @param P1 The first parameter controlling the disparity smoothness. See below.
    .   @param P2 The second parameter controlling the disparity smoothness. The larger the values are,
    .   the smoother the disparity is. P1 is the penalty on the disparity change by plus or minus 1
    .   between neighbor pixels. P2 is the penalty on the disparity change by more than 1 between neighbor
    .   pixels. The algorithm requires P2 \> P1 . See stereo_match.cpp sample where some reasonably good
    .   P1 and P2 values are shown (like 8\*number_of_image_channels\*SADWindowSize\*SADWindowSize and
    .   32\*number_of_image_channels\*SADWindowSize\*SADWindowSize , respectively).
    .   @param disp12MaxDiff Maximum allowed difference (in integer pixel units) in the left-right
    .   disparity check. Set it to a non-positive value to disable the check.
    .   @param preFilterCap Truncation value for the prefiltered image pixels. The algorithm first
    .   computes x-derivative at each pixel and clips its value by [-preFilterCap, preFilterCap] interval.
    .   The result values are passed to the Birchfield-Tomasi pixel cost function.
    .   @param uniquenessRatio Margin in percentage by which the best (minimum) computed cost function
    .   value should "win" the second best value to consider the found match correct. Normally, a value
    .   within the 5-15 range is good enough.
    .   @param speckleWindowSize Maximum size of smooth disparity regions to consider their noise speckles
    .   and invalidate. Set it to 0 to disable speckle filtering. Otherwise, set it somewhere in the
    .   50-200 range.
    .   @param speckleRange Maximum disparity variation within each connected component. If you do speckle
    .   filtering, set the parameter to a positive value, it will be implicitly multiplied by 16.
    .   Normally, 1 or 2 is good enough.
    .   @param mode Set it to StereoSGBM::MODE_HH to run the full-scale two-pass dynamic programming
    .   algorithm. It will consume O(W\*H\*numDisparities) bytes, which is large for 640x480 stereo and
    .   huge for HD-size pictures. By default, it is set to false .
    .   
    .   The first constructor initializes StereoSGBM with all the default parameters. So, you only have to
    .   set StereoSGBM::numDisparities at minimum. The second constructor enables you to set each parameter
    .   to a custom value.
    """
    pass

def stylization(src, dst=None, sigma_s=None, sigma_r=None): # real signature unknown; restored from __doc__
    """
    stylization(src[, dst[, sigma_s[, sigma_r]]]) -> dst
    .   @brief Stylization aims to produce digital imagery with a wide variety of effects not focused on
    .   photorealism. Edge-aware filters are ideal for stylization, as they can abstract regions of low
    .   contrast while preserving, or enhancing, high-contrast features.
    .   
    .   @param src Input 8-bit 3-channel image.
    .   @param dst Output image with the same size and type as src.
    .   @param sigma_s Range between 0 to 200.
    .   @param sigma_r Range between 0 to 1.
    """
    pass

def Subdiv2D(rect=None): # real signature unknown; restored from __doc__
    """
    Subdiv2D([rect]) -> <Subdiv2D object>
    .   creates an empty Subdiv2D object.
    .   To create a new empty Delaunay subdivision you need to use the initDelaunay() function.
    """
    pass

def subtract(src1, src2, dst=None, mask=None, dtype=None): # real signature unknown; restored from __doc__
    """
    subtract(src1, src2[, dst[, mask[, dtype]]]) -> dst
    .   @brief Calculates the per-element difference between two arrays or array and a scalar.
    .   
    .   The function subtract calculates:
    .   - Difference between two arrays, when both input arrays have the same size and the same number of
    .   channels:
    .   \f[\texttt{dst}(I) =  \texttt{saturate} ( \texttt{src1}(I) -  \texttt{src2}(I)) \quad \texttt{if mask}(I) \ne0\f]
    .   - Difference between an array and a scalar, when src2 is constructed from Scalar or has the same
    .   number of elements as `src1.channels()`:
    .   \f[\texttt{dst}(I) =  \texttt{saturate} ( \texttt{src1}(I) -  \texttt{src2} ) \quad \texttt{if mask}(I) \ne0\f]
    .   - Difference between a scalar and an array, when src1 is constructed from Scalar or has the same
    .   number of elements as `src2.channels()`:
    .   \f[\texttt{dst}(I) =  \texttt{saturate} ( \texttt{src1} -  \texttt{src2}(I) ) \quad \texttt{if mask}(I) \ne0\f]
    .   - The reverse difference between a scalar and an array in the case of `SubRS`:
    .   \f[\texttt{dst}(I) =  \texttt{saturate} ( \texttt{src2} -  \texttt{src1}(I) ) \quad \texttt{if mask}(I) \ne0\f]
    .   where I is a multi-dimensional index of array elements. In case of multi-channel arrays, each
    .   channel is processed independently.
    .   
    .   The first function in the list above can be replaced with matrix expressions:
    .   @code{.cpp}
    .   dst = src1 - src2;
    .   dst -= src1; // equivalent to subtract(dst, src1, dst);
    .   @endcode
    .   The input arrays and the output array can all have the same or different depths. For example, you
    .   can subtract to 8-bit unsigned arrays and store the difference in a 16-bit signed array. Depth of
    .   the output array is determined by dtype parameter. In the second and third cases above, as well as
    .   in the first case, when src1.depth() == src2.depth(), dtype can be set to the default -1. In this
    .   case the output array will have the same depth as the input array, be it src1, src2 or both.
    .   @note Saturation is not applied when the output array has the depth CV_32S. You may even get
    .   result of an incorrect sign in the case of overflow.
    .   @param src1 first input array or a scalar.
    .   @param src2 second input array or a scalar.
    .   @param dst output array of the same size and the same number of channels as the input array.
    .   @param mask optional operation mask; this is an 8-bit single channel array that specifies elements
    .   of the output array to be changed.
    .   @param dtype optional depth of the output array
    .   @sa  add, addWeighted, scaleAdd, Mat::convertTo
    """
    pass

def sumElems(src): # real signature unknown; restored from __doc__
    """
    sumElems(src) -> retval
    .   @brief Calculates the sum of array elements.
    .   
    .   The function cv::sum calculates and returns the sum of array elements,
    .   independently for each channel.
    .   @param src input array that must have from 1 to 4 channels.
    .   @sa  countNonZero, mean, meanStdDev, norm, minMaxLoc, reduce
    """
    pass

def SVBackSubst(w, u, vt, rhs, dst=None): # real signature unknown; restored from __doc__
    """
    SVBackSubst(w, u, vt, rhs[, dst]) -> dst
    .   wrap SVD::backSubst
    """
    pass

def SVDecomp(src, w=None, u=None, vt=None, flags=None): # real signature unknown; restored from __doc__
    """
    SVDecomp(src[, w[, u[, vt[, flags]]]]) -> w, u, vt
    .   wrap SVD::compute
    """
    pass

def textureFlattening(src, mask, dst=None, low_threshold=None, high_threshold=None, kernel_size=None): # real signature unknown; restored from __doc__
    """
    textureFlattening(src, mask[, dst[, low_threshold[, high_threshold[, kernel_size]]]]) -> dst
    .   @brief By retaining only the gradients at edge locations, before integrating with the Poisson solver, one
    .   washes out the texture of the selected region, giving its contents a flat aspect. Here Canny Edge
    .   Detector is used.
    .   
    .   @param src Input 8-bit 3-channel image.
    .   @param mask Input 8-bit 1 or 3-channel image.
    .   @param dst Output image with the same size and type as src.
    .   @param low_threshold Range from 0 to 100.
    .   @param high_threshold Value \> 100.
    .   @param kernel_size The size of the Sobel kernel to be used.
    .   
    .   **NOTE:**
    .   
    .   The algorithm assumes that the color of the source image is close to that of the destination. This
    .   assumption means that when the colors don't match, the source image color gets tinted toward the
    .   color of the destination image.
    """
    pass

def threshold(src, thresh, maxval, type, dst=None): # real signature unknown; restored from __doc__
    """
    threshold(src, thresh, maxval, type[, dst]) -> retval, dst
    .   @brief Applies a fixed-level threshold to each array element.
    .   
    .   The function applies fixed-level thresholding to a multiple-channel array. The function is typically
    .   used to get a bi-level (binary) image out of a grayscale image ( cv::compare could be also used for
    .   this purpose) or for removing a noise, that is, filtering out pixels with too small or too large
    .   values. There are several types of thresholding supported by the function. They are determined by
    .   type parameter.
    .   
    .   Also, the special values cv::THRESH_OTSU or cv::THRESH_TRIANGLE may be combined with one of the
    .   above values. In these cases, the function determines the optimal threshold value using the Otsu's
    .   or Triangle algorithm and uses it instead of the specified thresh . The function returns the
    .   computed threshold value. Currently, the Otsu's and Triangle methods are implemented only for 8-bit
    .   images.
    .   
    .   @note Input image should be single channel only in case of CV_THRESH_OTSU or CV_THRESH_TRIANGLE flags
    .   
    .   @param src input array (multiple-channel, 8-bit or 32-bit floating point).
    .   @param dst output array of the same size  and type and the same number of channels as src.
    .   @param thresh threshold value.
    .   @param maxval maximum value to use with the THRESH_BINARY and THRESH_BINARY_INV thresholding
    .   types.
    .   @param type thresholding type (see the cv::ThresholdTypes).
    .   
    .   @sa  adaptiveThreshold, findContours, compare, min, max
    """
    pass

def TickMeter(): # real signature unknown; restored from __doc__
    """
    TickMeter() -> <TickMeter object>
    .
    """
    pass

def trace(mtx): # real signature unknown; restored from __doc__
    """
    trace(mtx) -> retval
    .   @brief Returns the trace of a matrix.
    .   
    .   The function cv::trace returns the sum of the diagonal elements of the
    .   matrix mtx .
    .   \f[\mathrm{tr} ( \texttt{mtx} ) =  \sum _i  \texttt{mtx} (i,i)\f]
    .   @param mtx input matrix.
    """
    pass

def transform(src, m, dst=None): # real signature unknown; restored from __doc__
    """
    transform(src, m[, dst]) -> dst
    .   @brief Performs the matrix transformation of every array element.
    .   
    .   The function cv::transform performs the matrix transformation of every
    .   element of the array src and stores the results in dst :
    .   \f[\texttt{dst} (I) =  \texttt{m} \cdot \texttt{src} (I)\f]
    .   (when m.cols=src.channels() ), or
    .   \f[\texttt{dst} (I) =  \texttt{m} \cdot [ \texttt{src} (I); 1]\f]
    .   (when m.cols=src.channels()+1 )
    .   
    .   Every element of the N -channel array src is interpreted as N -element
    .   vector that is transformed using the M x N or M x (N+1) matrix m to
    .   M-element vector - the corresponding element of the output array dst .
    .   
    .   The function may be used for geometrical transformation of
    .   N -dimensional points, arbitrary linear color space transformation (such
    .   as various kinds of RGB to YUV transforms), shuffling the image
    .   channels, and so forth.
    .   @param src input array that must have as many channels (1 to 4) as
    .   m.cols or m.cols-1.
    .   @param dst output array of the same size and depth as src; it has as
    .   many channels as m.rows.
    .   @param m transformation 2x2 or 2x3 floating-point matrix.
    .   @sa perspectiveTransform, getAffineTransform, estimateAffine2D, warpAffine, warpPerspective
    """
    pass

def transpose(src, dst=None): # real signature unknown; restored from __doc__
    """
    transpose(src[, dst]) -> dst
    .   @brief Transposes a matrix.
    .   
    .   The function cv::transpose transposes the matrix src :
    .   \f[\texttt{dst} (i,j) =  \texttt{src} (j,i)\f]
    .   @note No complex conjugation is done in case of a complex matrix. It
    .   should be done separately if needed.
    .   @param src input array.
    .   @param dst output array of the same type as src.
    """
    pass

def triangulatePoints(projMatr1, projMatr2, projPoints1, projPoints2, points4D=None): # real signature unknown; restored from __doc__
    """
    triangulatePoints(projMatr1, projMatr2, projPoints1, projPoints2[, points4D]) -> points4D
    .   @brief Reconstructs points by triangulation.
    .   
    .   @param projMatr1 3x4 projection matrix of the first camera.
    .   @param projMatr2 3x4 projection matrix of the second camera.
    .   @param projPoints1 2xN array of feature points in the first image. In case of c++ version it can
    .   be also a vector of feature points or two-channel matrix of size 1xN or Nx1.
    .   @param projPoints2 2xN array of corresponding points in the second image. In case of c++ version
    .   it can be also a vector of feature points or two-channel matrix of size 1xN or Nx1.
    .   @param points4D 4xN array of reconstructed points in homogeneous coordinates.
    .   
    .   The function reconstructs 3-dimensional points (in homogeneous coordinates) by using their
    .   observations with a stereo camera. Projections matrices can be obtained from stereoRectify.
    .   
    .   @note
    .   Keep in mind that all input data should be of float type in order for this function to work.
    .   
    .   @sa
    .   reprojectImageTo3D
    """
    pass

def undistort(src, cameraMatrix, distCoeffs, dst=None, newCameraMatrix=None): # real signature unknown; restored from __doc__
    """
    undistort(src, cameraMatrix, distCoeffs[, dst[, newCameraMatrix]]) -> dst
    .   @brief Transforms an image to compensate for lens distortion.
    .   
    .   The function transforms an image to compensate radial and tangential lens distortion.
    .   
    .   The function is simply a combination of cv::initUndistortRectifyMap (with unity R ) and cv::remap
    .   (with bilinear interpolation). See the former function for details of the transformation being
    .   performed.
    .   
    .   Those pixels in the destination image, for which there is no correspondent pixels in the source
    .   image, are filled with zeros (black color).
    .   
    .   A particular subset of the source image that will be visible in the corrected image can be regulated
    .   by newCameraMatrix. You can use cv::getOptimalNewCameraMatrix to compute the appropriate
    .   newCameraMatrix depending on your requirements.
    .   
    .   The camera matrix and the distortion parameters can be determined using cv::calibrateCamera. If
    .   the resolution of images is different from the resolution used at the calibration stage, \f$f_x,
    .   f_y, c_x\f$ and \f$c_y\f$ need to be scaled accordingly, while the distortion coefficients remain
    .   the same.
    .   
    .   @param src Input (distorted) image.
    .   @param dst Output (corrected) image that has the same size and type as src .
    .   @param cameraMatrix Input camera matrix \f$A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ .
    .   @param distCoeffs Input vector of distortion coefficients
    .   \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$
    .   of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
    .   @param newCameraMatrix Camera matrix of the distorted image. By default, it is the same as
    .   cameraMatrix but you may additionally scale and shift the result by using a different matrix.
    """
    pass

def undistortPoints(src, cameraMatrix, distCoeffs, dst=None, R=None, P=None): # real signature unknown; restored from __doc__
    """
    undistortPoints(src, cameraMatrix, distCoeffs[, dst[, R[, P]]]) -> dst
    .   @brief Computes the ideal point coordinates from the observed point coordinates.
    .   
    .   The function is similar to cv::undistort and cv::initUndistortRectifyMap but it operates on a
    .   sparse set of points instead of a raster image. Also the function performs a reverse transformation
    .   to projectPoints. In case of a 3D object, it does not reconstruct its 3D coordinates, but for a
    .   planar object, it does, up to a translation vector, if the proper R is specified.
    .   
    .   For each observed point coordinate \f$(u, v)\f$ the function computes:
    .   \f[
    .   \begin{array}{l}
    .   x^{"}  \leftarrow (u - c_x)/f_x  \\
    .   y^{"}  \leftarrow (v - c_y)/f_y  \\
    .   (x',y') = undistort(x^{"},y^{"}, \texttt{distCoeffs}) \\
    .   {[X\,Y\,W]} ^T  \leftarrow R*[x' \, y' \, 1]^T  \\
    .   x  \leftarrow X/W  \\
    .   y  \leftarrow Y/W  \\
    .   \text{only performed if P is specified:} \\
    .   u'  \leftarrow x {f'}_x + {c'}_x  \\
    .   v'  \leftarrow y {f'}_y + {c'}_y
    .   \end{array}
    .   \f]
    .   
    .   where *undistort* is an approximate iterative algorithm that estimates the normalized original
    .   point coordinates out of the normalized distorted point coordinates ("normalized" means that the
    .   coordinates do not depend on the camera matrix).
    .   
    .   The function can be used for both a stereo camera head or a monocular camera (when R is empty).
    .   
    .   @param src Observed point coordinates, 1xN or Nx1 2-channel (CV_32FC2 or CV_64FC2).
    .   @param dst Output ideal point coordinates after undistortion and reverse perspective
    .   transformation. If matrix P is identity or omitted, dst will contain normalized point coordinates.
    .   @param cameraMatrix Camera matrix \f$\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\f$ .
    .   @param distCoeffs Input vector of distortion coefficients
    .   \f$(k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\f$
    .   of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
    .   @param R Rectification transformation in the object space (3x3 matrix). R1 or R2 computed by
    .   cv::stereoRectify can be passed here. If the matrix is empty, the identity transformation is used.
    .   @param P New camera matrix (3x3) or new projection matrix (3x4) \f$\begin{bmatrix} {f'}_x & 0 & {c'}_x & t_x \\ 0 & {f'}_y & {c'}_y & t_y \\ 0 & 0 & 1 & t_z \end{bmatrix}\f$. P1 or P2 computed by
    .   cv::stereoRectify can be passed here. If the matrix is empty, the identity new camera matrix is used.
    """
    pass

def useOpenVX(): # real signature unknown; restored from __doc__
    """
    useOpenVX() -> retval
    .
    """
    pass

def useOptimized(): # real signature unknown; restored from __doc__
    """
    useOptimized() -> retval
    .   @brief Returns the status of optimized code usage.
    .   
    .   The function returns true if the optimized code is enabled. Otherwise, it returns false.
    """
    pass

def validateDisparity(disparity, cost, minDisparity, numberOfDisparities, disp12MaxDisp=None): # real signature unknown; restored from __doc__
    """
    validateDisparity(disparity, cost, minDisparity, numberOfDisparities[, disp12MaxDisp]) -> disparity
    .
    """
    pass

def vconcat(src, dst=None): # real signature unknown; restored from __doc__
    """
    vconcat(src[, dst]) -> dst
    .   @overload
    .   @code{.cpp}
    .   std::vector<cv::Mat> matrices = { cv::Mat(1, 4, CV_8UC1, cv::Scalar(1)),
    .   cv::Mat(1, 4, CV_8UC1, cv::Scalar(2)),
    .   cv::Mat(1, 4, CV_8UC1, cv::Scalar(3)),};
    .   
    .   cv::Mat out;
    .   cv::vconcat( matrices, out );
    .   //out:
    .   //[1,   1,   1,   1;
    .   // 2,   2,   2,   2;
    .   // 3,   3,   3,   3]
    .   @endcode
    .   @param src input array or vector of matrices. all of the matrices must have the same number of cols and the same depth
    .   @param dst output array. It has the same number of cols and depth as the src, and the sum of rows of the src.
    .   same depth.
    """
    pass

def VideoCapture(): # real signature unknown; restored from __doc__
    """
    VideoCapture() -> <VideoCapture object>
    .   @brief Default constructor
    .   @note In @ref videoio_c "C API", when you finished working with video, release CvCapture structure with
    .   cvReleaseCapture(), or use Ptr\<CvCapture\> that calls cvReleaseCapture() automatically in the
    .   destructor.
    
    
    
    VideoCapture(filename) -> <VideoCapture object>
    .   @overload
    .   @brief  Open video file or a capturing device or a IP video stream for video capturing
    .   
    .   Same as VideoCapture(const String& filename, int apiPreference) but using default Capture API backends
    
    
    
    VideoCapture(filename, apiPreference) -> <VideoCapture object>
    .   @overload
    .   @brief  Open video file or a capturing device or a IP video stream for video capturing with API Preference
    .   
    .   @param filename it can be:
    .   - name of video file (eg. `video.avi`)
    .   - or image sequence (eg. `img_%02d.jpg`, which will read samples like `img_00.jpg, img_01.jpg, img_02.jpg, ...`)
    .   - or URL of video stream (eg. `protocol://host:port/script_name?script_params|auth`).
    .   Note that each video stream or IP camera feed has its own URL scheme. Please refer to the
    .   documentation of source stream to know the right URL.
    .   @param apiPreference preferred Capture API backends to use. Can be used to enforce a specific reader
    .   implementation if multiple are available: e.g. cv::CAP_FFMPEG or cv::CAP_IMAGES or cv::CAP_DSHOW.
    .   @sa The list of supported API backends cv::VideoCaptureAPIs
    
    
    
    VideoCapture(index) -> <VideoCapture object>
    .   @overload
    .   @brief  Open a camera for video capturing
    .   
    .   @param index camera_id + domain_offset (CAP_*) id of the video capturing device to open. To open default camera using default backend just pass 0.
    .   Use a `domain_offset` to enforce a specific reader implementation if multiple are available like cv::CAP_FFMPEG or cv::CAP_IMAGES or cv::CAP_DSHOW.
    .   e.g. to open Camera 1 using the MS Media Foundation API use `index = 1 + cv::CAP_MSMF`
    .   
    .   @sa The list of supported API backends cv::VideoCaptureAPIs
    """
    pass

def VideoWriter(): # real signature unknown; restored from __doc__
    """
    VideoWriter() -> <VideoWriter object>
    .   @brief Default constructors
    .   
    .   The constructors/functions initialize video writers.
    .   -   On Linux FFMPEG is used to write videos;
    .   -   On Windows FFMPEG or VFW is used;
    .   -   On MacOSX QTKit is used.
    
    
    
    VideoWriter(filename, fourcc, fps, frameSize[, isColor]) -> <VideoWriter object>
    .   @overload
    .   @param filename Name of the output video file.
    .   @param fourcc 4-character code of codec used to compress the frames. For example,
    .   VideoWriter::fourcc('P','I','M','1') is a MPEG-1 codec, VideoWriter::fourcc('M','J','P','G') is a
    .   motion-jpeg codec etc. List of codes can be obtained at [Video Codecs by
    .   FOURCC](http://www.fourcc.org/codecs.php) page. FFMPEG backend with MP4 container natively uses
    .   other values as fourcc code: see [ObjectType](http://www.mp4ra.org/codecs.html),
    .   so you may receive a warning message from OpenCV about fourcc code conversion.
    .   @param fps Framerate of the created video stream.
    .   @param frameSize Size of the video frames.
    .   @param isColor If it is not zero, the encoder will expect and encode color frames, otherwise it
    .   will work with grayscale frames (the flag is currently supported on Windows only).
    .   
    .   @b Tips:
    .   - With some backends `fourcc=-1` pops up the codec selection dialog from the system.
    .   - To save image sequence use a proper filename (eg. `img_%02d.jpg`) and `fourcc=0`
    .   OR `fps=0`. Use uncompressed image format (eg. `img_%02d.BMP`) to save raw frames.
    .   - Most codecs are lossy. If you want lossless video file you need to use a lossless codecs
    .   (eg. FFMPEG FFV1, Huffman HFYU, Lagarith LAGS, etc...)
    .   - If FFMPEG is enabled, using `codec=0; fps=0;` you can create an uncompressed (raw) video file.
    
    
    
    VideoWriter(filename, apiPreference, fourcc, fps, frameSize[, isColor]) -> <VideoWriter object>
    .   @overload
    .   The `apiPreference` parameter allows to specify API backends to use. Can be used to enforce a specific reader implementation
    .   if multiple are available: e.g. cv::CAP_FFMPEG or cv::CAP_GSTREAMER.
    """
    pass

def VideoWriter_fourcc(c1, c2, c3, c4): # real signature unknown; restored from __doc__
    """
    VideoWriter_fourcc(c1, c2, c3, c4) -> retval
    .   @brief Concatenates 4 chars to a fourcc code
    .   
    .   @return a fourcc code
    .   
    .   This static method constructs the fourcc code of the codec to be used in the constructor
    .   VideoWriter::VideoWriter or VideoWriter::open.
    """
    pass

def waitKey(delay=None): # real signature unknown; restored from __doc__
    """
    waitKey([, delay]) -> retval
    .   @brief Waits for a pressed key.
    .   
    .   The function waitKey waits for a key event infinitely (when \f$\texttt{delay}\leq 0\f$ ) or for delay
    .   milliseconds, when it is positive. Since the OS has a minimum time between switching threads, the
    .   function will not wait exactly delay ms, it will wait at least delay ms, depending on what else is
    .   running on your computer at that time. It returns the code of the pressed key or -1 if no key was
    .   pressed before the specified time had elapsed.
    .   
    .   @note
    .   
    .   This function is the only method in HighGUI that can fetch and handle events, so it needs to be
    .   called periodically for normal event processing unless HighGUI is used within an environment that
    .   takes care of event processing.
    .   
    .   @note
    .   
    .   The function only works if there is at least one HighGUI window created and the window is active.
    .   If there are several HighGUI windows, any of them can be active.
    .   
    .   @param delay Delay in milliseconds. 0 is the special value that means "forever".
    """
    pass

def waitKeyEx(delay=None): # real signature unknown; restored from __doc__
    """
    waitKeyEx([, delay]) -> retval
    .   @brief Similar to #waitKey, but returns full key code.
    .   
    .   @note
    .   
    .   Key code is implementation specific and depends on used backend: QT/GTK/Win32/etc
    """
    pass

def warpAffine(src, M, dsize, dst=None, flags=None, borderMode=None, borderValue=None): # real signature unknown; restored from __doc__
    """
    warpAffine(src, M, dsize[, dst[, flags[, borderMode[, borderValue]]]]) -> dst
    .   @brief Applies an affine transformation to an image.
    .   
    .   The function warpAffine transforms the source image using the specified matrix:
    .   
    .   \f[\texttt{dst} (x,y) =  \texttt{src} ( \texttt{M} _{11} x +  \texttt{M} _{12} y +  \texttt{M} _{13}, \texttt{M} _{21} x +  \texttt{M} _{22} y +  \texttt{M} _{23})\f]
    .   
    .   when the flag WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted
    .   with cv::invertAffineTransform and then put in the formula above instead of M. The function cannot
    .   operate in-place.
    .   
    .   @param src input image.
    .   @param dst output image that has the size dsize and the same type as src .
    .   @param M \f$2\times 3\f$ transformation matrix.
    .   @param dsize size of the output image.
    .   @param flags combination of interpolation methods (see cv::InterpolationFlags) and the optional
    .   flag WARP_INVERSE_MAP that means that M is the inverse transformation (
    .   \f$\texttt{dst}\rightarrow\texttt{src}\f$ ).
    .   @param borderMode pixel extrapolation method (see cv::BorderTypes); when
    .   borderMode=BORDER_TRANSPARENT, it means that the pixels in the destination image corresponding to
    .   the "outliers" in the source image are not modified by the function.
    .   @param borderValue value used in case of a constant border; by default, it is 0.
    .   
    .   @sa  warpPerspective, resize, remap, getRectSubPix, transform
    """
    pass

def warpPerspective(src, M, dsize, dst=None, flags=None, borderMode=None, borderValue=None): # real signature unknown; restored from __doc__
    """
    warpPerspective(src, M, dsize[, dst[, flags[, borderMode[, borderValue]]]]) -> dst
    .   @brief Applies a perspective transformation to an image.
    .   
    .   The function warpPerspective transforms the source image using the specified matrix:
    .   
    .   \f[\texttt{dst} (x,y) =  \texttt{src} \left ( \frac{M_{11} x + M_{12} y + M_{13}}{M_{31} x + M_{32} y + M_{33}} ,
    .   \frac{M_{21} x + M_{22} y + M_{23}}{M_{31} x + M_{32} y + M_{33}} \right )\f]
    .   
    .   when the flag WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted with invert
    .   and then put in the formula above instead of M. The function cannot operate in-place.
    .   
    .   @param src input image.
    .   @param dst output image that has the size dsize and the same type as src .
    .   @param M \f$3\times 3\f$ transformation matrix.
    .   @param dsize size of the output image.
    .   @param flags combination of interpolation methods (INTER_LINEAR or INTER_NEAREST) and the
    .   optional flag WARP_INVERSE_MAP, that sets M as the inverse transformation (
    .   \f$\texttt{dst}\rightarrow\texttt{src}\f$ ).
    .   @param borderMode pixel extrapolation method (BORDER_CONSTANT or BORDER_REPLICATE).
    .   @param borderValue value used in case of a constant border; by default, it equals 0.
    .   
    .   @sa  warpAffine, resize, remap, getRectSubPix, perspectiveTransform
    """
    pass

def watershed(image, markers): # real signature unknown; restored from __doc__
    """
    watershed(image, markers) -> markers
    .   @brief Performs a marker-based image segmentation using the watershed algorithm.
    .   
    .   The function implements one of the variants of watershed, non-parametric marker-based segmentation
    .   algorithm, described in @cite Meyer92 .
    .   
    .   Before passing the image to the function, you have to roughly outline the desired regions in the
    .   image markers with positive (\>0) indices. So, every region is represented as one or more connected
    .   components with the pixel values 1, 2, 3, and so on. Such markers can be retrieved from a binary
    .   mask using findContours and drawContours (see the watershed.cpp demo). The markers are "seeds" of
    .   the future image regions. All the other pixels in markers , whose relation to the outlined regions
    .   is not known and should be defined by the algorithm, should be set to 0's. In the function output,
    .   each pixel in markers is set to a value of the "seed" components or to -1 at boundaries between the
    .   regions.
    .   
    .   @note Any two neighbor connected components are not necessarily separated by a watershed boundary
    .   (-1's pixels); for example, they can touch each other in the initial marker image passed to the
    .   function.
    .   
    .   @param image Input 8-bit 3-channel image.
    .   @param markers Input/output 32-bit single-channel image (map) of markers. It should have the same
    .   size as image .
    .   
    .   @sa findContours
    .   
    .   @ingroup imgproc_misc
    """
    pass

# classes

class error(Exception):
    # no doc
    def __init__(self, *args, **kwargs): # real signature unknown
        pass

    __weakref__ = property(lambda self: object(), lambda self, v: None, lambda self: None)  # default
    """list of weak references to the object (if defined)"""



class UMat(object):
    """ OpenCV 3 UMat wrapper. Used for T-API support. """
    def context(self, *args, **kwargs): # real signature unknown
        """ Returns OpenCL context handle """
        pass

    def get(self, *args, **kwargs): # real signature unknown
        """ Returns numpy array """
        pass

    def handle(self, *args, **kwargs): # real signature unknown
        """ Returns UMat native handle """
        pass

    def isContinuous(self, *args, **kwargs): # real signature unknown
        """ Returns true if the matrix data is continuous """
        pass

    def isSubmatrix(self, *args, **kwargs): # real signature unknown
        """ Returns true if the matrix is a submatrix of another matrix """
        pass

    def queue(self, *args, **kwargs): # real signature unknown
        """ Returns OpenCL queue handle """
        pass

    def __init__(self, *args, **kwargs): # real signature unknown
        pass

    @staticmethod # known case of __new__
    def __new__(*args, **kwargs): # real signature unknown
        """ Create and return a new object.  See help(type) for accurate signature. """
        pass

    offset = property(lambda self: object(), lambda self, v: None, lambda self: None)  # default



# variables with complex values

__loader__ = __loader__

__spec__ = __spec__