opencv_dart library

Classes

AgastFeatureDetector
AgastFeatureDetector is a wrapper around the cv::AgastFeatureDetector.
AKAZE
AKAZE is a wrapper around the cv::AKAZE algorithm.
AlignMTB
AlignMTB for converts images to median threshold bitmaps. of type AlignMTB converts images to median threshold bitmaps (1 for pixels brighter than median luminance and 0 otherwise) and than aligns the resulting bitmaps using bit operations. For further details, please see: https://docs.opencv.org/master/d6/df5/group__photo__hdr.html https://docs.opencv.org/master/d7/db6/classcv_1_1AlignMTB.html https://docs.opencv.org/master/d6/df5/group__photo__hdr.html#ga2f1fafc885a5d79dbfb3542e08db0244
ArucoDetector
ArucoDetectorParameters
ArucoDictionary
AsyncArray
AverageHash
AverageHash is implementation of the AverageHash algorithm.
BackgroundSubtractorKNN
BackgroundSubtractorMOG2
BFMatcher
BFMatcher is a wrapper around the cv::BFMatcher.
BlockMeanHash
BlockMeanHash is implementation of the BlockMeanHash algorithm.
BRISK
BRISK is a wrapper around the cv::BRISK algorithm.
CascadeClassifier
ColorMomentHash
ColorMomentHash is implementation of the ColorMomentHash algorithm.
Contours
Contours2f
Contours2fIterator
Contours3f
Contours3fIterator
ContoursIterator
CvException
CvObject<T extends NativeType>
DMatch
DMatches
FastFeatureDetector
FastFeatureDetector is a wrapper around the cv::FastFeatureDetector.
Fisheye
FlannBasedMatcher
FlannBasedMatcher is a wrapper around the cv::FlannBasedMatcher.
GFTTDetector
GFTTDetector is a wrapper around the cv::GFTTDetector.
HOGDescriptor
ImgHashBase
KalmanFilter
KalmanFilter implements a standard Kalman filter http://en.wikipedia.org/wiki/Kalman_filter. However, you can modify transitionMatrix, controlMatrix, and measurementMatrix to get an extended Kalman filter functionality.
KAZE
KAZE is a wrapper around the cv::KAZE.
KeyPoint
KeyPoints
Layer
Layer is a wrapper around the cv::dnn::Layer algorithm.
ListPoint2f
Mat
MatType
MergeMertens
MergeMertens algorithm merge the ldr image should result in a HDR image. For further details, please see: https://docs.opencv.org/master/d6/df5/group__photo__hdr.html https://docs.opencv.org/master/d7/dd6/classcv_1_1MergeMertens.html https://docs.opencv.org/master/d6/df5/group__photo__hdr.html#ga79d59aa3cb3a7c664e59a4b5acc1ccb6
Moments
MSER
MSER is a wrapper around the cv::MSER.
MultiDMatches
Net
Net allows you to create and manipulate comprehensive artificial neural networks.
NewMarrHildrethHash
MarrHildrethHash is implementation of the MarrHildrethHash algorithm.
NewRadialVarianceHash
NewRadialVarianceHash is implementation of the NewRadialVarianceHash algorithm.
ORB
ORB is a wrapper around the cv::ORB.
PHash
PHash is implementation of the PHash algorithm.
Point
Point2f
Point3f
QRCodeDetector
Rect
Rects
Rng
RotatedRect
Scalar
SIFT
SIFT is a wrapper around the cv::SIFT.
SimpleBlobDetector
SimpleBlobDetector is a wrapper around the cv::SimpleBlobDetector.
SimpleBlobDetectorParams
SolvePnPMethod
Stitcher
High level image stitcher.
SVD
SVDCompute decomposes matrix and stores the results to user-provided matrices
Trackbar
TrackerMIL
Tracker is the base interface for object tracking.
VideoAccelerationType
@brief Video Acceleration type
VideoCapture
VideoWriter
Window
Window is a wrapper around OpenCV's "HighGUI" named windows. While OpenCV was designed for use in full-scale applications and can be used within functionally rich UI frameworks (such as Qt*, WinForms*, or Cocoa*) or without any UI at all, sometimes there it is required to try functionality quickly and visualize the results. This is what the HighGUI module has been designed for.

Constants

ADAPTIVE_THRESH_GAUSSIAN_C → const int
ADAPTIVE_THRESH_MEAN_C → const int
BORDER_CONSTANT → const int
BORDER_DEFAULT → const int
BORDER_ISOLATED → const int
BORDER_REFLECT → const int
BORDER_REFLECT101 → const int
BORDER_REFLECT_101 → const int
BORDER_REPLICATE → const int
BORDER_TRANSPARENT → const int
BORDER_WRAP → const int
CALIB_CB_ACCURACY → const int
CALIB_CB_ADAPTIVE_THRESH → const int
CALIB_CB_ASYMMETRIC_GRID → const int
CALIB_CB_CLUSTERING → const int
CALIB_CB_EXHAUSTIVE → const int
CALIB_CB_FAST_CHECK → const int
CALIB_CB_FILTER_QUADS → const int
CALIB_CB_LARGER → const int
CALIB_CB_MARKER → const int
CALIB_CB_NORMALIZE_IMAGE → const int
CALIB_CB_PLAIN → const int
CALIB_CB_SYMMETRIC_GRID → const int
CALIB_FIX_ASPECT_RATIO → const int
CALIB_FIX_FOCAL_LENGTH → const int
CALIB_FIX_INTRINSIC → const int
CALIB_FIX_K1 → const int
CALIB_FIX_K2 → const int
CALIB_FIX_K3 → const int
CALIB_FIX_K4 → const int
CALIB_FIX_K5 → const int
CALIB_FIX_K6 → const int
CALIB_FIX_PRINCIPAL_POINT → const int
CALIB_FIX_S1_S2_S3_S4 → const int
CALIB_FIX_TANGENT_DIST → const int
CALIB_FIX_TAUX_TAUY → const int
CALIB_NINTRINSIC → const int
CALIB_RATIONAL_MODEL → const int
CALIB_SAME_FOCAL_LENGTH → const int
CALIB_THIN_PRISM_MODEL → const int
CALIB_TILTED_MODEL → const int
CALIB_USE_EXTRINSIC_GUESS → const int
CALIB_USE_INTRINSIC_GUESS → const int
CALIB_USE_LU → const int
CALIB_USE_QR → const int
CALIB_ZERO_DISPARITY → const int
CALIB_ZERO_TANGENT_DIST → const int
CAP_ANDROID → const int
CAP_ANY → const int
CAP_ARAVIS → const int
CAP_AVFOUNDATION → const int
CAP_CMU1394 → const int
CAP_DC1394 → const int
CAP_DSHOW → const int
CAP_FFMPEG → const int
CAP_FIREWARE → const int
CAP_FIREWIRE → const int
CAP_GIGANETIX → const int
CAP_GPHOTO2 → const int
CAP_GSTREAMER → const int
CAP_IEEE1394 → const int
CAP_IMAGES → const int
CAP_INTEL_MFX → const int
CAP_INTELPERC → const int
CAP_INTELPERC_DEPTH_GENERATOR → const int
CAP_INTELPERC_DEPTH_MAP → const int
CAP_INTELPERC_GENERATORS_MASK → const int
CAP_INTELPERC_IMAGE → const int
CAP_INTELPERC_IMAGE_GENERATOR → const int
CAP_INTELPERC_IR_GENERATOR → const int
CAP_INTELPERC_IR_MAP → const int
CAP_INTELPERC_UVDEPTH_MAP → const int
CAP_MSMF → const int
CAP_OBSENSOR → const int
CAP_OBSENSOR_BGR_IMAGE → const int
CAP_OBSENSOR_DEPTH_GENERATOR → const int
CAP_OBSENSOR_DEPTH_MAP → const int
CAP_OBSENSOR_GENERATORS_MASK → const int
CAP_OBSENSOR_IMAGE_GENERATOR → const int
CAP_OBSENSOR_IR_GENERATOR → const int
CAP_OBSENSOR_IR_IMAGE → const int
CAP_OPENCV_MJPEG → const int
CAP_OPENNI → const int
CAP_OPENNI2 → const int
CAP_OPENNI2_ASTRA → const int
CAP_OPENNI2_ASUS → const int
CAP_OPENNI_ASUS → const int
CAP_OPENNI_BGR_IMAGE → const int
CAP_OPENNI_DEPTH_GENERATOR → const int
CAP_OPENNI_DEPTH_GENERATOR_BASELINE → const int
CAP_OPENNI_DEPTH_GENERATOR_FOCAL_LENGTH → const int
CAP_OPENNI_DEPTH_GENERATOR_PRESENT → const int
CAP_OPENNI_DEPTH_GENERATOR_REGISTRATION → const int
CAP_OPENNI_DEPTH_GENERATOR_REGISTRATION_ON → const int
CAP_OPENNI_DEPTH_MAP → const int
CAP_OPENNI_DISPARITY_MAP → const int
CAP_OPENNI_DISPARITY_MAP_32F → const int
CAP_OPENNI_GENERATORS_MASK → const int
CAP_OPENNI_GRAY_IMAGE → const int
CAP_OPENNI_IMAGE_GENERATOR → const int
CAP_OPENNI_IMAGE_GENERATOR_OUTPUT_MODE → const int
CAP_OPENNI_IMAGE_GENERATOR_PRESENT → const int
CAP_OPENNI_IR_GENERATOR → const int
CAP_OPENNI_IR_GENERATOR_PRESENT → const int
CAP_OPENNI_IR_IMAGE → const int
CAP_OPENNI_POINT_CLOUD_MAP → const int
CAP_OPENNI_QVGA_30HZ → const int
CAP_OPENNI_QVGA_60HZ → const int
CAP_OPENNI_SXGA_15HZ → const int
CAP_OPENNI_SXGA_30HZ → const int
CAP_OPENNI_VALID_DEPTH_MASK → const int
CAP_OPENNI_VGA_30HZ → const int
CAP_PROP_APERTURE → const int
CAP_PROP_ARAVIS_AUTOTRIGGER → const int
CAP_PROP_AUDIO_BASE_INDEX → const int
CAP_PROP_AUDIO_DATA_DEPTH → const int
CAP_PROP_AUDIO_POS → const int
CAP_PROP_AUDIO_SAMPLES_PER_SECOND → const int
CAP_PROP_AUDIO_SHIFT_NSEC → const int
CAP_PROP_AUDIO_STREAM → const int
CAP_PROP_AUDIO_SYNCHRONIZE → const int
CAP_PROP_AUDIO_TOTAL_CHANNELS → const int
CAP_PROP_AUDIO_TOTAL_STREAMS → const int
CAP_PROP_AUTO_EXPOSURE → const int
CAP_PROP_AUTO_WB → const int
CAP_PROP_AUTOFOCUS → const int
CAP_PROP_BACKEND → const int
CAP_PROP_BACKLIGHT → const int
CAP_PROP_BITRATE → const int
CAP_PROP_BRIGHTNESS → const int
CAP_PROP_BUFFERSIZE → const int
CAP_PROP_CHANNEL → const int
CAP_PROP_CODEC_EXTRADATA_INDEX → const int
CAP_PROP_CODEC_PIXEL_FORMAT → const int
CAP_PROP_CONTRAST → const int
CAP_PROP_CONVERT_RGB → const int
CAP_PROP_DC1394_MAX → const int
CAP_PROP_DC1394_MODE_AUTO → const int
CAP_PROP_DC1394_MODE_MANUAL → const int
CAP_PROP_DC1394_MODE_ONE_PUSH_AUTO → const int
CAP_PROP_DC1394_OFF → const int
CAP_PROP_EXPOSURE → const int
CAP_PROP_EXPOSUREPROGRAM → const int
CAP_PROP_FOCUS → const int
CAP_PROP_FORMAT → const int
CAP_PROP_FOURCC → const int
CAP_PROP_FPS → const int
CAP_PROP_FRAME_COUNT → const int
CAP_PROP_FRAME_HEIGHT → const int
CAP_PROP_FRAME_TYPE → const int
CAP_PROP_FRAME_WIDTH → const int
CAP_PROP_GAIN → const int
CAP_PROP_GAMMA → const int
CAP_PROP_GIGA_FRAME_HEIGH_MAX → const int
CAP_PROP_GIGA_FRAME_OFFSET_X → const int
CAP_PROP_GIGA_FRAME_OFFSET_Y → const int
CAP_PROP_GIGA_FRAME_SENS_HEIGH → const int
CAP_PROP_GIGA_FRAME_SENS_WIDTH → const int
CAP_PROP_GIGA_FRAME_WIDTH_MAX → const int
CAP_PROP_GPHOTO2_COLLECT_MSGS → const int
CAP_PROP_GPHOTO2_FLUSH_MSGS → const int
CAP_PROP_GPHOTO2_PREVIEW → const int
CAP_PROP_GPHOTO2_RELOAD_CONFIG → const int
CAP_PROP_GPHOTO2_RELOAD_ON_CHANGE → const int
CAP_PROP_GPHOTO2_WIDGET_ENUMERATE → const int
CAP_PROP_GSTREAMER_QUEUE_LENGTH → const int
CAP_PROP_GUID → const int
CAP_PROP_HUE → const int
CAP_PROP_HW_ACCELERATION → const int
CAP_PROP_HW_ACCELERATION_USE_OPENCL → const int
CAP_PROP_HW_DEVICE → const int
CAP_PROP_IMAGES_BASE → const int
CAP_PROP_IMAGES_LAST → const int
CAP_PROP_INTELPERC_DEPTH_CONFIDENCE_THRESHOLD → const int
CAP_PROP_INTELPERC_DEPTH_FOCAL_LENGTH_HORZ → const int
CAP_PROP_INTELPERC_DEPTH_FOCAL_LENGTH_VERT → const int
CAP_PROP_INTELPERC_DEPTH_LOW_CONFIDENCE_VALUE → const int
CAP_PROP_INTELPERC_DEPTH_SATURATION_VALUE → const int
CAP_PROP_INTELPERC_PROFILE_COUNT → const int
CAP_PROP_INTELPERC_PROFILE_IDX → const int
CAP_PROP_IOS_DEVICE_EXPOSURE → const int
CAP_PROP_IOS_DEVICE_FLASH → const int
CAP_PROP_IOS_DEVICE_FOCUS → const int
CAP_PROP_IOS_DEVICE_TORCH → const int
CAP_PROP_IOS_DEVICE_WHITEBALANCE → const int
CAP_PROP_IRIS → const int
CAP_PROP_ISO_SPEED → const int
CAP_PROP_LRF_HAS_KEY_FRAME → const int
CAP_PROP_MODE → const int
CAP_PROP_MONOCHROME → const int
CAP_PROP_N_THREADS → const int
CAP_PROP_OBSENSOR_INTRINSIC_CX → const int
CAP_PROP_OBSENSOR_INTRINSIC_CY → const int
CAP_PROP_OBSENSOR_INTRINSIC_FX → const int
CAP_PROP_OBSENSOR_INTRINSIC_FY → const int
CAP_PROP_OPEN_TIMEOUT_MSEC → const int
CAP_PROP_OPENNI2_MIRROR → const int
CAP_PROP_OPENNI2_SYNC → const int
CAP_PROP_OPENNI_APPROX_FRAME_SYNC → const int
CAP_PROP_OPENNI_BASELINE → const int
CAP_PROP_OPENNI_CIRCLE_BUFFER → const int
CAP_PROP_OPENNI_FOCAL_LENGTH → const int
CAP_PROP_OPENNI_FRAME_MAX_DEPTH → const int
CAP_PROP_OPENNI_GENERATOR_PRESENT → const int
CAP_PROP_OPENNI_MAX_BUFFER_SIZE → const int
CAP_PROP_OPENNI_MAX_TIME_DURATION → const int
CAP_PROP_OPENNI_OUTPUT_MODE → const int
CAP_PROP_OPENNI_REGISTRATION → const int
CAP_PROP_OPENNI_REGISTRATION_ON → const int
CAP_PROP_ORIENTATION_AUTO → const int
CAP_PROP_ORIENTATION_META → const int
CAP_PROP_PAN → const int
CAP_PROP_POS_AVI_RATIO → const int
CAP_PROP_POS_FRAMES → const int
CAP_PROP_POS_MSEC → const int
CAP_PROP_PVAPI_BINNINGX → const int
CAP_PROP_PVAPI_BINNINGY → const int
CAP_PROP_PVAPI_DECIMATIONHORIZONTAL → const int
CAP_PROP_PVAPI_DECIMATIONVERTICAL → const int
CAP_PROP_PVAPI_FRAMESTARTTRIGGERMODE → const int
CAP_PROP_PVAPI_MULTICASTIP → const int
CAP_PROP_PVAPI_PIXELFORMAT → const int
CAP_PROP_READ_TIMEOUT_MSEC → const int
CAP_PROP_RECTIFICATION → const int
CAP_PROP_ROLL → const int
CAP_PROP_SAR_DEN → const int
CAP_PROP_SAR_NUM → const int
CAP_PROP_SATURATION → const int
CAP_PROP_SETTINGS → const int
CAP_PROP_SHARPNESS → const int
CAP_PROP_SPEED → const int
CAP_PROP_STREAM_OPEN_TIME_USEC → const int
CAP_PROP_TEMPERATURE → const int
CAP_PROP_TILT → const int
CAP_PROP_TRIGGER → const int
CAP_PROP_TRIGGER_DELAY → const int
CAP_PROP_VIDEO_STREAM → const int
CAP_PROP_VIDEO_TOTAL_CHANNELS → const int
CAP_PROP_VIEWFINDER → const int
CAP_PROP_WB_TEMPERATURE → const int
CAP_PROP_WHITE_BALANCE_BLUE_U → const int
CAP_PROP_WHITE_BALANCE_RED_V → const int
CAP_PROP_XI_ACQ_BUFFER_SIZE → const int
CAP_PROP_XI_ACQ_BUFFER_SIZE_UNIT → const int
CAP_PROP_XI_ACQ_FRAME_BURST_COUNT → const int
CAP_PROP_XI_ACQ_TIMING_MODE → const int
CAP_PROP_XI_ACQ_TRANSPORT_BUFFER_COMMIT → const int
CAP_PROP_XI_ACQ_TRANSPORT_BUFFER_SIZE → const int
CAP_PROP_XI_AE_MAX_LIMIT → const int
CAP_PROP_XI_AEAG → const int
CAP_PROP_XI_AEAG_LEVEL → const int
CAP_PROP_XI_AEAG_ROI_HEIGHT → const int
CAP_PROP_XI_AEAG_ROI_OFFSET_X → const int
CAP_PROP_XI_AEAG_ROI_OFFSET_Y → const int
CAP_PROP_XI_AEAG_ROI_WIDTH → const int
CAP_PROP_XI_AG_MAX_LIMIT → const int
CAP_PROP_XI_APPLY_CMS → const int
CAP_PROP_XI_AUTO_BANDWIDTH_CALCULATION → const int
CAP_PROP_XI_AUTO_WB → const int
CAP_PROP_XI_AVAILABLE_BANDWIDTH → const int
CAP_PROP_XI_BINNING_HORIZONTAL → const int
CAP_PROP_XI_BINNING_PATTERN → const int
CAP_PROP_XI_BINNING_SELECTOR → const int
CAP_PROP_XI_BINNING_VERTICAL → const int
CAP_PROP_XI_BPC → const int
CAP_PROP_XI_BUFFER_POLICY → const int
CAP_PROP_XI_BUFFERS_QUEUE_SIZE → const int
CAP_PROP_XI_CC_MATRIX_00 → const int
CAP_PROP_XI_CC_MATRIX_01 → const int
CAP_PROP_XI_CC_MATRIX_02 → const int
CAP_PROP_XI_CC_MATRIX_03 → const int
CAP_PROP_XI_CC_MATRIX_10 → const int
CAP_PROP_XI_CC_MATRIX_11 → const int
CAP_PROP_XI_CC_MATRIX_12 → const int
CAP_PROP_XI_CC_MATRIX_13 → const int
CAP_PROP_XI_CC_MATRIX_20 → const int
CAP_PROP_XI_CC_MATRIX_21 → const int
CAP_PROP_XI_CC_MATRIX_22 → const int
CAP_PROP_XI_CC_MATRIX_23 → const int
CAP_PROP_XI_CC_MATRIX_30 → const int
CAP_PROP_XI_CC_MATRIX_31 → const int
CAP_PROP_XI_CC_MATRIX_32 → const int
CAP_PROP_XI_CC_MATRIX_33 → const int
CAP_PROP_XI_CHIP_TEMP → const int
CAP_PROP_XI_CMS → const int
CAP_PROP_XI_COLOR_FILTER_ARRAY → const int
CAP_PROP_XI_COLUMN_FPN_CORRECTION → const int
CAP_PROP_XI_COOLING → const int
CAP_PROP_XI_COUNTER_SELECTOR → const int
CAP_PROP_XI_COUNTER_VALUE → const int
CAP_PROP_XI_DATA_FORMAT → const int
CAP_PROP_XI_DEBOUNCE_EN → const int
CAP_PROP_XI_DEBOUNCE_POL → const int
CAP_PROP_XI_DEBOUNCE_T0 → const int
CAP_PROP_XI_DEBOUNCE_T1 → const int
CAP_PROP_XI_DEBUG_LEVEL → const int
CAP_PROP_XI_DECIMATION_HORIZONTAL → const int
CAP_PROP_XI_DECIMATION_PATTERN → const int
CAP_PROP_XI_DECIMATION_SELECTOR → const int
CAP_PROP_XI_DECIMATION_VERTICAL → const int
CAP_PROP_XI_DEFAULT_CC_MATRIX → const int
CAP_PROP_XI_DEVICE_MODEL_ID → const int
CAP_PROP_XI_DEVICE_RESET → const int
CAP_PROP_XI_DEVICE_SN → const int
CAP_PROP_XI_DOWNSAMPLING → const int
CAP_PROP_XI_DOWNSAMPLING_TYPE → const int
CAP_PROP_XI_EXP_PRIORITY → const int
CAP_PROP_XI_EXPOSURE → const int
CAP_PROP_XI_EXPOSURE_BURST_COUNT → const int
CAP_PROP_XI_FFS_ACCESS_KEY → const int
CAP_PROP_XI_FFS_FILE_ID → const int
CAP_PROP_XI_FFS_FILE_SIZE → const int
CAP_PROP_XI_FRAMERATE → const int
CAP_PROP_XI_FREE_FFS_SIZE → const int
CAP_PROP_XI_GAIN → const int
CAP_PROP_XI_GAIN_SELECTOR → const int
CAP_PROP_XI_GAMMAC → const int
CAP_PROP_XI_GAMMAY → const int
CAP_PROP_XI_GPI_LEVEL → const int
CAP_PROP_XI_GPI_MODE → const int
CAP_PROP_XI_GPI_SELECTOR → const int
CAP_PROP_XI_GPO_MODE → const int
CAP_PROP_XI_GPO_SELECTOR → const int
CAP_PROP_XI_HDR → const int
CAP_PROP_XI_HDR_KNEEPOINT_COUNT → const int
CAP_PROP_XI_HDR_T1 → const int
CAP_PROP_XI_HDR_T2 → const int
CAP_PROP_XI_HEIGHT → const int
CAP_PROP_XI_HOUS_BACK_SIDE_TEMP → const int
CAP_PROP_XI_HOUS_TEMP → const int
CAP_PROP_XI_HW_REVISION → const int
CAP_PROP_XI_IMAGE_BLACK_LEVEL → const int
CAP_PROP_XI_IMAGE_DATA_BIT_DEPTH → const int
CAP_PROP_XI_IMAGE_DATA_FORMAT → const int
CAP_PROP_XI_IMAGE_DATA_FORMAT_RGB32_ALPHA → const int
CAP_PROP_XI_IMAGE_IS_COLOR → const int
CAP_PROP_XI_IMAGE_PAYLOAD_SIZE → const int
CAP_PROP_XI_IS_COOLED → const int
CAP_PROP_XI_IS_DEVICE_EXIST → const int
CAP_PROP_XI_KNEEPOINT1 → const int
CAP_PROP_XI_KNEEPOINT2 → const int
CAP_PROP_XI_LED_MODE → const int
CAP_PROP_XI_LED_SELECTOR → const int
CAP_PROP_XI_LENS_APERTURE_VALUE → const int
CAP_PROP_XI_LENS_FEATURE → const int
CAP_PROP_XI_LENS_FEATURE_SELECTOR → const int
CAP_PROP_XI_LENS_FOCAL_LENGTH → const int
CAP_PROP_XI_LENS_FOCUS_DISTANCE → const int
CAP_PROP_XI_LENS_FOCUS_MOVE → const int
CAP_PROP_XI_LENS_FOCUS_MOVEMENT_VALUE → const int
CAP_PROP_XI_LENS_MODE → const int
CAP_PROP_XI_LIMIT_BANDWIDTH → const int
CAP_PROP_XI_LUT_EN → const int
CAP_PROP_XI_LUT_INDEX → const int
CAP_PROP_XI_LUT_VALUE → const int
CAP_PROP_XI_MANUAL_WB → const int
CAP_PROP_XI_OFFSET_X → const int
CAP_PROP_XI_OFFSET_Y → const int
CAP_PROP_XI_OUTPUT_DATA_BIT_DEPTH → const int
CAP_PROP_XI_OUTPUT_DATA_PACKING → const int
CAP_PROP_XI_OUTPUT_DATA_PACKING_TYPE → const int
CAP_PROP_XI_RECENT_FRAME → const int
CAP_PROP_XI_REGION_MODE → const int
CAP_PROP_XI_REGION_SELECTOR → const int
CAP_PROP_XI_ROW_FPN_CORRECTION → const int
CAP_PROP_XI_SENSOR_BOARD_TEMP → const int
CAP_PROP_XI_SENSOR_CLOCK_FREQ_HZ → const int
CAP_PROP_XI_SENSOR_CLOCK_FREQ_INDEX → const int
CAP_PROP_XI_SENSOR_DATA_BIT_DEPTH → const int
CAP_PROP_XI_SENSOR_FEATURE_SELECTOR → const int
CAP_PROP_XI_SENSOR_FEATURE_VALUE → const int
CAP_PROP_XI_SENSOR_MODE → const int
CAP_PROP_XI_SENSOR_OUTPUT_CHANNEL_COUNT → const int
CAP_PROP_XI_SENSOR_TAPS → const int
CAP_PROP_XI_SHARPNESS → const int
CAP_PROP_XI_SHUTTER_TYPE → const int
CAP_PROP_XI_TARGET_TEMP → const int
CAP_PROP_XI_TEST_PATTERN → const int
CAP_PROP_XI_TEST_PATTERN_GENERATOR_SELECTOR → const int
CAP_PROP_XI_TIMEOUT → const int
CAP_PROP_XI_TRANSPORT_PIXEL_FORMAT → const int
CAP_PROP_XI_TRG_DELAY → const int
CAP_PROP_XI_TRG_SELECTOR → const int
CAP_PROP_XI_TRG_SOFTWARE → const int
CAP_PROP_XI_TRG_SOURCE → const int
CAP_PROP_XI_TS_RST_MODE → const int
CAP_PROP_XI_TS_RST_SOURCE → const int
CAP_PROP_XI_USED_FFS_SIZE → const int
CAP_PROP_XI_WB_KB → const int
CAP_PROP_XI_WB_KG → const int
CAP_PROP_XI_WB_KR → const int
CAP_PROP_XI_WIDTH → const int
CAP_PROP_ZOOM → const int
CAP_PVAPI → const int
CAP_PVAPI_DECIMATION_2OUTOF16 → const int
CAP_PVAPI_DECIMATION_2OUTOF4 → const int
CAP_PVAPI_DECIMATION_2OUTOF8 → const int
CAP_PVAPI_DECIMATION_OFF → const int
CAP_PVAPI_FSTRIGMODE_FIXEDRATE → const int
CAP_PVAPI_FSTRIGMODE_FREERUN → const int
CAP_PVAPI_FSTRIGMODE_SOFTWARE → const int
CAP_PVAPI_FSTRIGMODE_SYNCIN1 → const int
CAP_PVAPI_FSTRIGMODE_SYNCIN2 → const int
CAP_PVAPI_PIXELFORMAT_BAYER16 → const int
CAP_PVAPI_PIXELFORMAT_BAYER8 → const int
CAP_PVAPI_PIXELFORMAT_BGR24 → const int
CAP_PVAPI_PIXELFORMAT_BGRA32 → const int
CAP_PVAPI_PIXELFORMAT_MONO16 → const int
CAP_PVAPI_PIXELFORMAT_MONO8 → const int
CAP_PVAPI_PIXELFORMAT_RGB24 → const int
CAP_PVAPI_PIXELFORMAT_RGBA32 → const int
CAP_QT → const int
CAP_REALSENSE → const int
CAP_UEYE → const int
CAP_UNICAP → const int
CAP_V4L → const int
CAP_V4L2 → const int
CAP_VFW → const int
CAP_WINRT → const int
CAP_XIAPI → const int
CAP_XINE → const int
CC_STAT_AREA → const int
CC_STAT_HEIGHT → const int
CC_STAT_LEFT → const int
CC_STAT_MAX → const int
CC_STAT_TOP → const int
CC_STAT_WIDTH → const int
CCL_BBDT → const int
CCL_BOLELLI → const int
CCL_DEFAULT → const int
CCL_GRANA → const int
CCL_SAUF → const int
CCL_SPAGHETTI → const int
CCL_WU → const int
CHAIN_APPROX_NONE → const int
CHAIN_APPROX_SIMPLE → const int
CHAIN_APPROX_TC89_KCOS → const int
CHAIN_APPROX_TC89_L1 → const int
CMP_EQ → const int
CMP_GE → const int
CMP_GT → const int
CMP_LE → const int
CMP_LT → const int
CMP_NE → const int
COLOR_BayerBG2BGR → const int
COLOR_BayerBG2BGR_EA → const int
COLOR_BayerBG2BGR_VNG → const int
COLOR_BayerBG2BGRA → const int
COLOR_BayerBG2GRAY → const int
COLOR_BayerBG2RGB → const int
COLOR_BayerBG2RGB_EA → const int
COLOR_BayerBG2RGB_VNG → const int
COLOR_BayerBG2RGBA → const int
COLOR_BayerBGGR2BGR → const int
COLOR_BayerBGGR2BGR_EA → const int
COLOR_BayerBGGR2BGR_VNG → const int
COLOR_BayerBGGR2BGRA → const int
COLOR_BayerBGGR2GRAY → const int
COLOR_BayerBGGR2RGB → const int
COLOR_BayerBGGR2RGB_EA → const int
COLOR_BayerBGGR2RGB_VNG → const int
COLOR_BayerBGGR2RGBA → const int
COLOR_BayerGB2BGR → const int
COLOR_BayerGB2BGR_EA → const int
COLOR_BayerGB2BGR_VNG → const int
COLOR_BayerGB2BGRA → const int
COLOR_BayerGB2GRAY → const int
COLOR_BayerGB2RGB → const int
COLOR_BayerGB2RGB_EA → const int
COLOR_BayerGB2RGB_VNG → const int
COLOR_BayerGB2RGBA → const int
COLOR_BayerGBRG2BGR → const int
COLOR_BayerGBRG2BGR_EA → const int
COLOR_BayerGBRG2BGR_VNG → const int
COLOR_BayerGBRG2BGRA → const int
COLOR_BayerGBRG2GRAY → const int
COLOR_BayerGBRG2RGB → const int
COLOR_BayerGBRG2RGB_EA → const int
COLOR_BayerGBRG2RGB_VNG → const int
COLOR_BayerGBRG2RGBA → const int
COLOR_BayerGR2BGR → const int
COLOR_BayerGR2BGR_EA → const int
COLOR_BayerGR2BGR_VNG → const int
COLOR_BayerGR2BGRA → const int
COLOR_BayerGR2GRAY → const int
COLOR_BayerGR2RGB → const int
COLOR_BayerGR2RGB_EA → const int
COLOR_BayerGR2RGB_VNG → const int
COLOR_BayerGR2RGBA → const int
COLOR_BayerGRBG2BGR → const int
COLOR_BayerGRBG2BGR_EA → const int
COLOR_BayerGRBG2BGR_VNG → const int
COLOR_BayerGRBG2BGRA → const int
COLOR_BayerGRBG2GRAY → const int
COLOR_BayerGRBG2RGB → const int
COLOR_BayerGRBG2RGB_EA → const int
COLOR_BayerGRBG2RGB_VNG → const int
COLOR_BayerGRBG2RGBA → const int
COLOR_BayerRG2BGR → const int
COLOR_BayerRG2BGR_EA → const int
COLOR_BayerRG2BGR_VNG → const int
COLOR_BayerRG2BGRA → const int
COLOR_BayerRG2GRAY → const int
COLOR_BayerRG2RGB → const int
COLOR_BayerRG2RGB_EA → const int
COLOR_BayerRG2RGB_VNG → const int
COLOR_BayerRG2RGBA → const int
COLOR_BayerRGGB2BGR → const int
COLOR_BayerRGGB2BGR_EA → const int
COLOR_BayerRGGB2BGR_VNG → const int
COLOR_BayerRGGB2BGRA → const int
COLOR_BayerRGGB2GRAY → const int
COLOR_BayerRGGB2RGB → const int
COLOR_BayerRGGB2RGB_EA → const int
COLOR_BayerRGGB2RGB_VNG → const int
COLOR_BayerRGGB2RGBA → const int
COLOR_BGR2BGR555 → const int
COLOR_BGR2BGR565 → const int
COLOR_BGR2BGRA → const int
COLOR_BGR2GRAY → const int
COLOR_BGR2HLS → const int
COLOR_BGR2HLS_FULL → const int
COLOR_BGR2HSV → const int
COLOR_BGR2HSV_FULL → const int
COLOR_BGR2Lab → const int
COLOR_BGR2Luv → const int
COLOR_BGR2RGB → const int
COLOR_BGR2RGBA → const int
COLOR_BGR2XYZ → const int
COLOR_BGR2YCrCb → const int
COLOR_BGR2YUV → const int
COLOR_BGR2YUV_I420 → const int
COLOR_BGR2YUV_IYUV → const int
COLOR_BGR2YUV_UYNV → const int
COLOR_BGR2YUV_UYVY → const int
COLOR_BGR2YUV_Y422 → const int
COLOR_BGR2YUV_YUNV → const int
COLOR_BGR2YUV_YUY2 → const int
COLOR_BGR2YUV_YUYV → const int
COLOR_BGR2YUV_YV12 → const int
COLOR_BGR2YUV_YVYU → const int
COLOR_BGR5552BGR → const int
COLOR_BGR5552BGRA → const int
COLOR_BGR5552GRAY → const int
COLOR_BGR5552RGB → const int
COLOR_BGR5552RGBA → const int
COLOR_BGR5652BGR → const int
COLOR_BGR5652BGRA → const int
COLOR_BGR5652GRAY → const int
COLOR_BGR5652RGB → const int
COLOR_BGR5652RGBA → const int
COLOR_BGRA2BGR → const int
COLOR_BGRA2BGR555 → const int
COLOR_BGRA2BGR565 → const int
COLOR_BGRA2GRAY → const int
COLOR_BGRA2RGB → const int
COLOR_BGRA2RGBA → const int
COLOR_BGRA2YUV_I420 → const int
COLOR_BGRA2YUV_IYUV → const int
COLOR_BGRA2YUV_UYNV → const int
COLOR_BGRA2YUV_UYVY → const int
COLOR_BGRA2YUV_Y422 → const int
COLOR_BGRA2YUV_YUNV → const int
COLOR_BGRA2YUV_YUY2 → const int
COLOR_BGRA2YUV_YUYV → const int
COLOR_BGRA2YUV_YV12 → const int
COLOR_BGRA2YUV_YVYU → const int
COLOR_COLORCVT_MAX → const int
COLOR_GRAY2BGR → const int
COLOR_GRAY2BGR555 → const int
COLOR_GRAY2BGR565 → const int
COLOR_GRAY2BGRA → const int
COLOR_GRAY2RGB → const int
COLOR_GRAY2RGBA → const int
COLOR_HLS2BGR → const int
COLOR_HLS2BGR_FULL → const int
COLOR_HLS2RGB → const int
COLOR_HLS2RGB_FULL → const int
COLOR_HSV2BGR → const int
COLOR_HSV2BGR_FULL → const int
COLOR_HSV2RGB → const int
COLOR_HSV2RGB_FULL → const int
COLOR_Lab2BGR → const int
COLOR_Lab2LBGR → const int
COLOR_Lab2LRGB → const int
COLOR_Lab2RGB → const int
COLOR_LBGR2Lab → const int
COLOR_LBGR2Luv → const int
COLOR_LRGB2Lab → const int
COLOR_LRGB2Luv → const int
COLOR_Luv2BGR → const int
COLOR_Luv2LBGR → const int
COLOR_Luv2LRGB → const int
COLOR_Luv2RGB → const int
COLOR_mRGBA2RGBA → const int
COLOR_RGB2BGR → const int
COLOR_RGB2BGR555 → const int
COLOR_RGB2BGR565 → const int
COLOR_RGB2BGRA → const int
COLOR_RGB2GRAY → const int
COLOR_RGB2HLS → const int
COLOR_RGB2HLS_FULL → const int
COLOR_RGB2HSV → const int
COLOR_RGB2HSV_FULL → const int
COLOR_RGB2Lab → const int
COLOR_RGB2Luv → const int
COLOR_RGB2RGBA → const int
COLOR_RGB2XYZ → const int
COLOR_RGB2YCrCb → const int
COLOR_RGB2YUV → const int
COLOR_RGB2YUV_I420 → const int
COLOR_RGB2YUV_IYUV → const int
COLOR_RGB2YUV_UYNV → const int
COLOR_RGB2YUV_UYVY → const int
COLOR_RGB2YUV_Y422 → const int
COLOR_RGB2YUV_YUNV → const int
COLOR_RGB2YUV_YUY2 → const int
COLOR_RGB2YUV_YUYV → const int
COLOR_RGB2YUV_YV12 → const int
COLOR_RGB2YUV_YVYU → const int
COLOR_RGBA2BGR → const int
COLOR_RGBA2BGR555 → const int
COLOR_RGBA2BGR565 → const int
COLOR_RGBA2BGRA → const int
COLOR_RGBA2GRAY → const int
COLOR_RGBA2mRGBA → const int
COLOR_RGBA2RGB → const int
COLOR_RGBA2YUV_I420 → const int
COLOR_RGBA2YUV_IYUV → const int
COLOR_RGBA2YUV_UYNV → const int
COLOR_RGBA2YUV_UYVY → const int
COLOR_RGBA2YUV_Y422 → const int
COLOR_RGBA2YUV_YUNV → const int
COLOR_RGBA2YUV_YUY2 → const int
COLOR_RGBA2YUV_YUYV → const int
COLOR_RGBA2YUV_YV12 → const int
COLOR_RGBA2YUV_YVYU → const int
COLOR_XYZ2BGR → const int
COLOR_XYZ2RGB → const int
COLOR_YCrCb2BGR → const int
COLOR_YCrCb2RGB → const int
COLOR_YUV2BGR → const int
COLOR_YUV2BGR_I420 → const int
COLOR_YUV2BGR_IYUV → const int
COLOR_YUV2BGR_NV12 → const int
COLOR_YUV2BGR_NV21 → const int
COLOR_YUV2BGR_UYNV → const int
COLOR_YUV2BGR_UYVY → const int
COLOR_YUV2BGR_Y422 → const int
COLOR_YUV2BGR_YUNV → const int
COLOR_YUV2BGR_YUY2 → const int
COLOR_YUV2BGR_YUYV → const int
COLOR_YUV2BGR_YV12 → const int
COLOR_YUV2BGR_YVYU → const int
COLOR_YUV2BGRA_I420 → const int
COLOR_YUV2BGRA_IYUV → const int
COLOR_YUV2BGRA_NV12 → const int
COLOR_YUV2BGRA_NV21 → const int
COLOR_YUV2BGRA_UYNV → const int
COLOR_YUV2BGRA_UYVY → const int
COLOR_YUV2BGRA_Y422 → const int
COLOR_YUV2BGRA_YUNV → const int
COLOR_YUV2BGRA_YUY2 → const int
COLOR_YUV2BGRA_YUYV → const int
COLOR_YUV2BGRA_YV12 → const int
COLOR_YUV2BGRA_YVYU → const int
COLOR_YUV2GRAY_420 → const int
COLOR_YUV2GRAY_I420 → const int
COLOR_YUV2GRAY_IYUV → const int
COLOR_YUV2GRAY_NV12 → const int
COLOR_YUV2GRAY_NV21 → const int
COLOR_YUV2GRAY_UYNV → const int
COLOR_YUV2GRAY_UYVY → const int
COLOR_YUV2GRAY_Y422 → const int
COLOR_YUV2GRAY_YUNV → const int
COLOR_YUV2GRAY_YUY2 → const int
COLOR_YUV2GRAY_YUYV → const int
COLOR_YUV2GRAY_YV12 → const int
COLOR_YUV2GRAY_YVYU → const int
COLOR_YUV2RGB → const int
COLOR_YUV2RGB_I420 → const int
COLOR_YUV2RGB_IYUV → const int
COLOR_YUV2RGB_NV12 → const int
COLOR_YUV2RGB_NV21 → const int
COLOR_YUV2RGB_UYNV → const int
COLOR_YUV2RGB_UYVY → const int
COLOR_YUV2RGB_Y422 → const int
COLOR_YUV2RGB_YUNV → const int
COLOR_YUV2RGB_YUY2 → const int
COLOR_YUV2RGB_YUYV → const int
COLOR_YUV2RGB_YV12 → const int
COLOR_YUV2RGB_YVYU → const int
COLOR_YUV2RGBA_I420 → const int
COLOR_YUV2RGBA_IYUV → const int
COLOR_YUV2RGBA_NV12 → const int
COLOR_YUV2RGBA_NV21 → const int
COLOR_YUV2RGBA_UYNV → const int
COLOR_YUV2RGBA_UYVY → const int
COLOR_YUV2RGBA_Y422 → const int
COLOR_YUV2RGBA_YUNV → const int
COLOR_YUV2RGBA_YUY2 → const int
COLOR_YUV2RGBA_YUYV → const int
COLOR_YUV2RGBA_YV12 → const int
COLOR_YUV2RGBA_YVYU → const int
COLOR_YUV420p2BGR → const int
COLOR_YUV420p2BGRA → const int
COLOR_YUV420p2GRAY → const int
COLOR_YUV420p2RGB → const int
COLOR_YUV420p2RGBA → const int
COLOR_YUV420sp2BGR → const int
COLOR_YUV420sp2BGRA → const int
COLOR_YUV420sp2GRAY → const int
COLOR_YUV420sp2RGB → const int
COLOR_YUV420sp2RGBA → const int
COLORMAP_AUTUMN → const int
COLORMAP_BONE → const int
COLORMAP_CIVIDIS → const int
COLORMAP_COOL → const int
COLORMAP_DEEPGREEN → const int
COLORMAP_HOT → const int
COLORMAP_HSV → const int
COLORMAP_INFERNO → const int
COLORMAP_JET → const int
COLORMAP_MAGMA → const int
COLORMAP_OCEAN → const int
COLORMAP_PARULA → const int
COLORMAP_PINK → const int
COLORMAP_PLASMA → const int
COLORMAP_RAINBOW → const int
COLORMAP_SPRING → const int
COLORMAP_SUMMER → const int
COLORMAP_TURBO → const int
COLORMAP_TWILIGHT → const int
COLORMAP_TWILIGHT_SHIFTED → const int
COLORMAP_VIRIDIS → const int
COLORMAP_WINTER → const int
CONTOURS_MATCH_I1 → const int
CONTOURS_MATCH_I2 → const int
CONTOURS_MATCH_I3 → const int
COVAR_COLS → const int
COVAR_NORMAL → const int
COVAR_ROWS → const int
COVAR_SCALE → const int
COVAR_SCRAMBLED → const int
COVAR_USE_AVG → const int
CV_2PI → const double
CV__CAP_PROP_LATEST → const int
CV__VIDEOWRITER_PROP_LATEST → const int
CV_F32_MAX → const double
CV_F64_MAX → const double
CV_I16_MAX → const int
CV_I16_MIN → const int
CV_I32_MAX → const int
CV_I32_MIN → const int
CV_I8_MAX → const int
CV_I8_MIN → const int
CV_LOG2 → const double
CV_PI → const double
CV_U16_MAX → const int
CV_U16_MIN → const int
CV_U32_MAX → const int
CV_U32_MIN → const int
CV_U8_MAX → const int
CV_U8_MIN → const int
DCT_INVERSE → const int
DCT_ROWS → const int
DECOMP_CHOLESKY → const int
DECOMP_EIG → const int
DECOMP_LU → const int
DECOMP_NORMAL → const int
DECOMP_QR → const int
DECOMP_SVD → const int
DFT_COMPLEX_INPUT → const int
DFT_COMPLEX_OUTPUT → const int
DFT_INVERSE → const int
DFT_REAL_OUTPUT → const int
DFT_ROWS → const int
DFT_SCALE → const int
DIST_C → const int
DIST_FAIR → const int
DIST_HUBER → const int
DIST_L1 → const int
DIST_L12 → const int
DIST_L2 → const int
DIST_LABEL_CCOMP → const int
DIST_LABEL_PIXEL → const int
DIST_MASK_3 → const int
DIST_MASK_5 → const int
DIST_MASK_PRECISE → const int
DIST_USER → const int
DIST_WELSCH → const int
DNN_BACKEND_CANN → const int
DNN_BACKEND_CUDA → const int
DNN_BACKEND_DEFAULT → const int
DNN_BACKEND_HALIDE → const int
DNN_BACKEND_INFERENCE_ENGINE → const int
DNN_BACKEND_OPENCV → const int
DNN_BACKEND_TIMVX → const int
DNN_BACKEND_VKCOM → const int
DNN_BACKEND_WEBNN → const int
DNN_TARGET_CPU → const int
DNN_TARGET_CPU_FP16 → const int
Only the ARM platform is supported. Low precision computing, accelerate model inference.
DNN_TARGET_CUDA → const int
DNN_TARGET_CUDA_FP16 → const int
DNN_TARGET_FPGA → const int
FPGA device with CPU fallbacks using Inference Engine's Heterogeneous plugin.
DNN_TARGET_HDDL → const int
DNN_TARGET_MYRIAD → const int
DNN_TARGET_NPU → const int
DNN_TARGET_OPENCL → const int
DNN_TARGET_OPENCL_FP16 → const int
DNN_TARGET_VULKAN → const int
FILLED → const int
FILTER_SCHARR → const int
FLOODFILL_FIXED_RANGE → const int
FLOODFILL_MASK_ONLY → const int
FONT_HERSHEY_COMPLEX → const int
FONT_HERSHEY_COMPLEX_SMALL → const int
FONT_HERSHEY_DUPLEX → const int
FONT_HERSHEY_PLAIN → const int
FONT_HERSHEY_SCRIPT_COMPLEX → const int
FONT_HERSHEY_SCRIPT_SIMPLEX → const int
FONT_HERSHEY_SIMPLEX → const int
FONT_HERSHEY_TRIPLEX → const int
FONT_ITALIC → const int
GC_BGD → const int
GC_EVAL → const int
GC_EVAL_FREEZE_MODEL → const int
GC_FGD → const int
GC_INIT_WITH_MASK → const int
GC_INIT_WITH_RECT → const int
GC_PR_BGD → const int
GC_PR_FGD → const int
GEMM_1_T → const int
GEMM_2_T → const int
GEMM_3_T → const int
HISTCMP_BHATTACHARYYA → const int
HISTCMP_CHISQR → const int
HISTCMP_CHISQR_ALT → const int
HISTCMP_CORREL → const int
HISTCMP_HELLINGER → const int
HISTCMP_INTERSECT → const int
HISTCMP_KL_DIV → const int
HOMOGRAPY_ALL_POINTS → const int
HOMOGRAPY_LMEDS → const int
HOMOGRAPY_RANSAC → const int
HOUGH_GRADIENT → const int
HOUGH_GRADIENT_ALT → const int
HOUGH_MULTI_SCALE → const int
HOUGH_PROBABILISTIC → const int
HOUGH_STANDARD → const int
IMREAD_ANYCOLOR → const int
IMREAD_ANYDEPTH → const int
IMREAD_COLOR → const int
IMREAD_GRAYSCALE → const int
IMREAD_IGNORE_ORIENTATION → const int
IMREAD_LOAD_GDAL → const int
IMREAD_REDUCED_COLOR_2 → const int
IMREAD_REDUCED_COLOR_4 → const int
IMREAD_REDUCED_COLOR_8 → const int
IMREAD_REDUCED_GRAYSCALE_2 → const int
IMREAD_REDUCED_GRAYSCALE_4 → const int
IMREAD_REDUCED_GRAYSCALE_8 → const int
IMREAD_UNCHANGED → const int
IMWRITE_AVIF_DEPTH → const int
IMWRITE_AVIF_QUALITY → const int
IMWRITE_AVIF_SPEED → const int
IMWRITE_EXR_COMPRESSION → const int
IMWRITE_EXR_COMPRESSION_B44 → const int
IMWRITE_EXR_COMPRESSION_B44A → const int
IMWRITE_EXR_COMPRESSION_DWAA → const int
IMWRITE_EXR_COMPRESSION_DWAB → const int
IMWRITE_EXR_COMPRESSION_NO → const int
IMWRITE_EXR_COMPRESSION_PIZ → const int
IMWRITE_EXR_COMPRESSION_PXR24 → const int
IMWRITE_EXR_COMPRESSION_RLE → const int
IMWRITE_EXR_COMPRESSION_ZIP → const int
IMWRITE_EXR_COMPRESSION_ZIPS → const int
IMWRITE_EXR_DWA_COMPRESSION_LEVEL → const int
IMWRITE_EXR_TYPE → const int
IMWRITE_EXR_TYPE_FLOAT → const int
IMWRITE_EXR_TYPE_HALF → const int
IMWRITE_HDR_COMPRESSION → const int
IMWRITE_HDR_COMPRESSION_NONE → const int
IMWRITE_HDR_COMPRESSION_RLE → const int
IMWRITE_JPEG2000_COMPRESSION_X1000 → const int
IMWRITE_JPEG_CHROMA_QUALITY → const int
IMWRITE_JPEG_LUMA_QUALITY → const int
IMWRITE_JPEG_OPTIMIZE → const int
IMWRITE_JPEG_PROGRESSIVE → const int
IMWRITE_JPEG_QUALITY → const int
IMWRITE_JPEG_RST_INTERVAL → const int
IMWRITE_JPEG_SAMPLING_FACTOR → const int
IMWRITE_JPEG_SAMPLING_FACTOR_411 → const int
IMWRITE_JPEG_SAMPLING_FACTOR_420 → const int
IMWRITE_JPEG_SAMPLING_FACTOR_422 → const int
IMWRITE_JPEG_SAMPLING_FACTOR_440 → const int
IMWRITE_JPEG_SAMPLING_FACTOR_444 → const int
IMWRITE_PAM_FORMAT_BLACKANDWHITE → const int
IMWRITE_PAM_FORMAT_GRAYSCALE → const int
IMWRITE_PAM_FORMAT_GRAYSCALE_ALPHA → const int
IMWRITE_PAM_FORMAT_NULL → const int
IMWRITE_PAM_FORMAT_RGB → const int
IMWRITE_PAM_FORMAT_RGB_ALPHA → const int
IMWRITE_PAM_TUPLETYPE → const int
IMWRITE_PNG_BILEVEL → const int
IMWRITE_PNG_COMPRESSION → const int
IMWRITE_PNG_STRATEGY → const int
IMWRITE_PNG_STRATEGY_DEFAULT → const int
IMWRITE_PNG_STRATEGY_FILTERED → const int
IMWRITE_PNG_STRATEGY_FIXED → const int
IMWRITE_PNG_STRATEGY_HUFFMAN_ONLY → const int
IMWRITE_PNG_STRATEGY_RLE → const int
IMWRITE_PXM_BINARY → const int
IMWRITE_TIFF_COMPRESSION → const int
IMWRITE_TIFF_RESUNIT → const int
IMWRITE_TIFF_XDPI → const int
IMWRITE_TIFF_YDPI → const int
IMWRITE_WEBP_QUALITY → const int
INPAINT_NS → const int
INPAINT_TELEA → const int
INTER_AREA → const int
INTER_BITS → const int
INTER_BITS2 → const int
INTER_CUBIC → const int
INTER_LANCZOS4 → const int
INTER_LINEAR → const int
INTER_LINEAR_EXACT → const int
INTER_MAX → const int
INTER_NEAREST → const int
INTER_NEAREST_EXACT → const int
INTER_TAB_SIZE → const int
INTER_TAB_SIZE2 → const int
INTERSECT_FULL → const int
INTERSECT_NONE → const int
INTERSECT_PARTIAL → const int
KMEANS_PP_CENTERS → const int
KMEANS_RANDOM_CENTERS → const int
KMEANS_USE_INITIAL_LABELS → const int
LINE_4 → const int
LINE_8 → const int
LINE_AA → const int
LMEDS → const int
LSD_REFINE_ADV → const int
LSD_REFINE_NONE → const int
LSD_REFINE_STD → const int
MARKER_CROSS → const int
MARKER_DIAMOND → const int
MARKER_SQUARE → const int
MARKER_STAR → const int
MARKER_TILTED_CROSS → const int
MARKER_TRIANGLE_DOWN → const int
MARKER_TRIANGLE_UP → const int
MIXED_CLONE → const int
MONOCHROME_TRANSFER → const int
MORPH_BLACKHAT → const int
MORPH_CLOSE → const int
MORPH_CROSS → const int
MORPH_DILATE → const int
MORPH_ELLIPSE → const int
MORPH_ERODE → const int
MORPH_GRADIENT → const int
MORPH_HITMISS → const int
MORPH_OPEN → const int
MORPH_RECT → const int
MORPH_TOPHAT → const int
MOTION_AFFINE → const int
MOTION_EUCLIDEAN → const int
MOTION_HOMOGRAPHY → const int
MOTION_TRANSLATION → const int
NORM_HAMMING → const int
NORM_HAMMING2 → const int
NORM_INF → const int
NORM_L1 → const int
NORM_L2 → const int
NORM_L2SQR → const int
NORM_MINMAX → const int
NORM_RELATIVE → const int
NORM_TYPE_MASK → const int
NORMAL_CLONE → const int
NORMCONV_FILTER → const int
OPTFLOW_FARNEBACK_GAUSSIAN → const int
OPTFLOW_LK_GET_MIN_EIGENVALS → const int
OPTFLOW_USE_INITIAL_FLOW → const int
RANSAC → const int
RECURS_FILTER → const int
REDUCE_AVG → const int
REDUCE_MAX → const int
REDUCE_MIN → const int
REDUCE_SUM → const int
REDUCE_SUM2 → const int
RETR_CCOMP → const int
RETR_EXTERNAL → const int
RETR_FLOODFILL → const int
RETR_LIST → const int
RETR_TREE → const int
RHO → const int
RNG_DIST_NORMAL → const int
RNG_DIST_UNIFORM → const int
ROTATE_180 → const int
ROTATE_90_CLOCKWISE → const int
ROTATE_90_COUNTERCLOCKWISE → const int
SORT_ASCENDING → const int
SORT_DESCENDING → const int
SORT_EVERY_COLUMN → const int
SORT_EVERY_ROW → const int
TERM_COUNT → const int
TERM_EPS → const int
TERM_MAX_ITER → const int
THRESH_BINARY → const int
THRESH_BINARY_INV → const int
THRESH_MASK → const int
THRESH_OTSU → const int
THRESH_TOZERO → const int
THRESH_TOZERO_INV → const int
THRESH_TRIANGLE → const int
THRESH_TRUNC → const int
TM_CCOEFF → const int
TM_CCOEFF_NORMED → const int
TM_CCORR → const int
TM_CCORR_NORMED → const int
TM_SQDIFF → const int
TM_SQDIFF_NORMED → const int
USAC_ACCURATE → const int
USAC_DEFAULT → const int
USAC_FAST → const int
USAC_FM_8PTS → const int
USAC_MAGSAC → const int
USAC_PARALLEL → const int
USAC_PROSAC → const int
VIDEOWRITER_PROP_DEPTH → const int
VIDEOWRITER_PROP_FRAMEBYTES → const int
VIDEOWRITER_PROP_HW_ACCELERATION → const int
VIDEOWRITER_PROP_HW_ACCELERATION_USE_OPENCL → const int
VIDEOWRITER_PROP_HW_DEVICE → const int
VIDEOWRITER_PROP_IS_COLOR → const int
VIDEOWRITER_PROP_KEY_FLAG → const int
VIDEOWRITER_PROP_KEY_INTERVAL → const int
VIDEOWRITER_PROP_NSTRIPES → const int
VIDEOWRITER_PROP_QUALITY → const int
VIDEOWRITER_PROP_RAW_VIDEO → const int
WARP_FILL_OUTLIERS → const int
WARP_INVERSE_MAP → const int
WARP_POLAR_LINEAR → const int
WARP_POLAR_LOG → const int

Functions

absDiff(Mat src1, Mat src2, Mat dst) → void
AbsDiff calculates the per-element absolute difference between two arrays or between an array and a scalar.
accumulate(InputArray src, InputOutputArray dst, {InputArray? mask}) → void
NewCLAHE returns a new CLAHE algorithm
accumulateProduct(InputArray src1, InputArray src2, InputOutputArray dst, {InputArray? mask}) → void
Adds the per-element product of two input images to the accumulator image.
accumulateSquare(InputArray src, InputOutputArray dst, {InputArray? mask}) → void
Adds the square of a source image to the accumulator image.
accumulateWeighted(InputArray src, InputOutputArray dst, double alpha, {InputArray? mask}) → void
Updates a running average.
adaptiveThreshold(InputArray src, OutputArray dst, double maxValue, int adaptiveMethod, int thresholdType, int blockSize, double C) → void
AdaptiveThreshold applies a fixed-level threshold to each array element.
add(Mat src1, Mat src2, Mat dst) → void
Add calculates the per-element sum of two arrays or an array and a scalar.
addWeighted(InputArray src1, double alpha, InputArray src2, double beta, double gamma, OutputArray dst, {int dtype = -1}) → void
AddWeighted calculates the weighted sum of two arrays.
applyColorMap(InputArray src, OutputArray dst, int colormap) → void
ApplyColorMap applies a GNU Octave/MATLAB equivalent colormap on a given image. colormap: ColormapTypes For further details, please see: https:///docs.opencv.org/master/d3/d50/group__imgproc__colormap.html#gadf478a5e5ff49d8aa24e726ea6f65d15
applyCustomColorMap(InputArray src, OutputArray dst, InputArray userColor) → void
ApplyCustomColorMap applies a custom defined colormap on a given image.
approxPolyDP(List<Point> curve, double epsilon, bool closed) → List<Point>
ApproxPolyDP approximates a polygonal curve(s) with the specified precision.
arcLength(List<Point> curve, bool closed) → double
ArcLength calculates a contour perimeter or a curve length.
arrowedLine(InputOutputArray img, Point pt1, Point pt2, Scalar color, {int thickness = 1, int line_type = 8, int shift = 0, double tipLength = 0.1}) → void
ArrowedLine draws a arrow segment pointing from the first point to the second one.
arucoDrawDetectedMarkers(Mat img, List<List<Point2f>> markerCorners, List<int> markerIds, Scalar borderColor) → void
arucoGenerateImageMarker(PredefinedDictionaryType dictionaryId, int id, int sidePixels, Mat img, int borderBits) → void
batchDistance(InputArray src1, InputArray src2, OutputArray dist, int dtype, OutputArray nidx, {int normType = NORM_L2, int K = 0, InputArray? mask, int update = 0, bool crosscheck = false}) → void
BatchDistance is a naive nearest neighbor finder.
bilateralFilter(Mat src, Mat dst, int diameter, double sigmaColor, double sigmaSpace) → void
BilateralFilter applies a bilateral filter to an image.
bitwise_and(InputArray src1, InputArray src2, OutputArray dst, {InputArray? mask}) → void
BitwiseAnd 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.
bitwise_not(InputArray src, OutputArray dst, {InputArray? mask}) → void
BitwiseNot inverts every bit of an array.
bitwise_or(InputArray src1, InputArray src2, OutputArray dst, {InputArray? mask}) → void
BitwiseOr calculates the per-element bit-wise disjunction of two arrays or an array and a scalar.
bitwise_xor(InputArray src1, InputArray src2, OutputArray dst, {InputArray? mask}) → void
BitwiseXor calculates the per-element bit-wise "exclusive or" operation on two arrays or an array and a scalar.
blobFromImage(InputArray image, {double scalefactor = 1.0, Size? size, Scalar? mean, bool swapRB = false, bool crop = false, int ddepth = MatType.CV_32F}) → Mat
Creates 4-dimensional blob from image. Optionally resizes and crops image from center, subtract mean values, scales values by scalefactor, swap Blue and Red channels.
blobFromImages(List<Mat> images, {Mat? blob, double scalefactor = 1.0, Size? size, Scalar? mean, bool swapRB = false, bool crop = false, int ddepth = MatType.CV_32F}) → Mat
Creates 4-dimensional blob from series of images. Optionally resizes and crops images from center, subtract mean values, scales values by scalefactor, swap Blue and Red channels. https://docs.opencv.org/4.x/d6/d0f/group__dnn.html#ga0b7b7c3c530b747ef738178835e1e70f
blur(Mat src, Mat dst, Size ksize) → void
Blur blurs an image Mat using a normalized box filter.
borderInterpolate(int p, int len, int borderType) → int
BorderInterpolate computes the source location of an extrapolated pixel.
boundingRect(List<Point> points) → Rect
BoundingRect calculates the up-right bounding rectangle of a point set.
boxFilter(Mat src, Mat dst, int depth, Size ksize) → void
BoxFilter blurs an image using the box filter.
boxPoints(RotatedRect rect, Mat pts) → void
BoxPoints finds the four vertices of a rotated rect. Useful to draw the rotated rectangle.
calcBackProject(List<Mat> src, List<int> channels, Mat hist, Mat backProject, List<double> ranges, {bool uniform = true}) → void
CalcBackProject calculates the back projection of a histogram.
calcCovarMatrix(InputArray samples, OutputArray covar, InputOutputArray mean, int flags, {int ctype = MatType.CV_64F}) → void
CalcCovarMatrix calculates the covariance matrix of a set of vectors.
calcHist(List<Mat> src, List<int> channels, Mat mask, Mat hist, List<int> histSize, List<double> ranges, {bool accumulate = false}) → void
CalcHist Calculates a histogram of a set of images
calcOpticalFlowFarneback(InputArray prev, InputArray next, InputOutputArray flow, double pyr_scale, int levels, int winsize, int iterations, int poly_n, double poly_sigma, int flags) → void
Apply computes a foreground mask using the current BackgroundSubtractorMOG2.
calcOpticalFlowPyrLK(InputArray prevImg, InputArray nextImg, InputArray prevPts, InputOutputArray nextPts, OutputArray status, OutputArray err, {Size winSize = (21, 21), int maxLevel = 3, TermCriteria? criteria, int flags = 0, double minEigThreshold = 1e-4}) → void
CalcOpticalFlowPyrLK calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade method with pyramids.
calibrateCamera(Contours3f objectPoints, Contours2f imagePoints, Size imageSize, InputOutputArray cameraMatrix, InputOutputArray distCoeffs, {Mat? rvecs, Mat? tvecs, int flags = 0, TermCriteria? criteria}) → (double, Mat, Mat, Mat, Mat)
Canny(Mat image, OutputArray edges, double threshold1, double threshold2, {int apertureSize = 3, bool L2gradient = false}) → void
Canny finds edges in an image using the Canny algorithm. 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
cartToPolar(InputArray x, InputArray y, OutputArray magnitude, OutputArray angle, {bool angleInDegrees = false}) → void
CartToPolar calculates the magnitude and angle of 2D vectors.
checkRange(InputArray a, {bool quiet = true, double minVal = -CV_F64_MAX, double maxVal = CV_F64_MAX}) → (bool, Point)
CheckRange checks every element of an input array for invalid values.
circle(InputOutputArray img, Point center, int radius, Scalar color, {int thickness = 1, int lineType = LINE_8, int shift = 0}) → void
CircleWithParams draws a circle.
clipLine((int, int) imgSize, (int, int) pt1, (int, int) pt2) → bool
ClipLine clips the line against the image rectangle. For further details, please see: https:///docs.opencv.org/master/d6/d6e/group__imgproc__draw.html#gaf483cb46ad6b049bc35ec67052ef1c2c
colorChange(InputArray src, InputArray mask, {double red_mul = 1.0, double green_mul = 1.0, double blue_mul = 1.0}) → Mat
ColorChange mix two differently colored versions of an image seamlessly. For further details, please see: https://docs.opencv.org/master/df/da0/group__photo__clone.html#ga6684f35dc669ff6196a7c340dc73b98e
compare(InputArray src1, InputArray src2, OutputArray dst, int cmpop) → void
Compare performs the per-element comparison of two arrays or an array and scalar value.
compareHist(Mat hist1, Mat hist2, {int method = 0}) → double
CompareHist Compares two histograms. mode: HistCompMethods For further details, please see: https:///docs.opencv.org/master/d6/dc7/group__imgproc__hist.html#gaf4190090efa5c47cb367cf97a9a519bd
completeSymm(InputOutputArray m, {bool lowerToUpper = false}) → void
CompleteSymm copies the lower or the upper half of a square matrix to its another half.
connectedComponents(Mat image, Mat labels, int connectivity, int ltype, int ccltype) → int
ConnectedComponents computes the connected components labeled image of boolean image.
connectedComponentsWithStats(Mat src, Mat labels, Mat stats, Mat centroids, int connectivity, int ltype, int ccltype) → int
ConnectedComponentsWithStats computes the connected components labeled image of boolean image and also produces a statistics output for each label.
contourArea(List<Point> contour) → double
ContourArea calculates a contour area.
convertScaleAbs(InputArray src, OutputArray dst, {double alpha = 1, double beta = 0}) → void
ConvertScaleAbs scales, calculates absolute values, and converts the result to 8-bit.
convexHull(List<Point> points, Mat hull, {bool clockwise = false, bool returnPoints = true}) → void
ConvexHull finds the convex hull of a point set.
convexityDefects(List<Point> contour, Mat hull, Mat result) → void
ConvexityDefects finds the convexity defects of a contour.
copyMakeBorder(InputArray src, OutputArray dst, int top, int bottom, int left, int right, int borderType, {Scalar? value}) → void
CopyMakeBorder forms a border around an image (applies padding).
cornerSubPix(InputArray image, InputOutputArray corners, Size winSize, Size zeroZone, TermCriteria criteria) → void
CornerSubPix Refines the corner locations. The function iterates to find the sub-pixel accurate location of corners or radial saddle points.
countNonZero(Mat src) → int
CountNonZero counts non-zero array elements.
createBackgroundSubtractorMOG2({int history = 500, double varThreshold = 16, bool detectShadows = true}) → BackgroundSubtractorMOG2
NewBackgroundSubtractorMOG2 returns a new BackgroundSubtractor algorithm of type MOG2. MOG2 is a Gaussian Mixture-based Background/Foreground Segmentation Algorithm.
cvRun(CvStatus func()) → void
cvtColor(Mat src, Mat dst, int code) → void
CvtColor converts an image from one color space to another. It converts the src Mat image to the dst Mat using the code param containing the desired ColorConversionCode color space.
dct(InputArray src, OutputArray dst, {int flags = 0}) → void
DCT performs a forward or inverse discrete Cosine transform of 1D or 2D array.
defaultCvErrorCallback(int code, Pointer<Char> func_name, Pointer<Char> err_msg, Pointer<Char> file_name, int line, Pointer<Void> userdata) → void
destroyAllWindows() → void
destroy all windows.
detailEnhance(InputArray src, {double sigma_s = 10, double sigma_r = 0.15}) → Mat
DetailEnhance filter enhances the details of a particular image For further details, please see: https://docs.opencv.org/4.x/df/dac/group__photo__render.html#ga0de660cb6f371a464a74c7b651415975
determinant(InputArray mtx) → double
Determinant returns the determinant of a square floating-point matrix.
dft(InputArray src, OutputArray dst, {int flags = 0, int nonzeroRows = 0}) → void
DFT performs a forward or inverse Discrete Fourier Transform (DFT) of a 1D or 2D floating-point array.
dilate(Mat src, Mat dst, Mat kernel, {({int x, int y}) anchor = (x: -1, y: -1), int iterations = 1, int borderType = BORDER_CONSTANT, Scalar? borderValue}) → void
Dilate dilates an image by using a specific structuring element.
distanceTransform(Mat src, Mat dst, Mat labels, int distanceType, int maskSize, int labelType) → void
DistanceTransform Calculates the distance to the closest zero pixel for each pixel of the source image.
divide(InputArray src1, InputArray src2, OutputArray dst, {double scale = 1, int dtype = -1}) → void
Divide performs the per-element division on two arrays or an array and a scalar.
drawChessboardCorners(InputOutputArray image, Size patternSize, InputArray corners, bool patternWasFound) → Mat
drawContours(InputOutputArray image, Contours contours, int contourIdx, Scalar color, {int thickness = 1, int lineType = LINE_8, InputArray? hierarchy, int maxLevel = 0x3f3f3f3f, Point? offset}) → void
FindHomography finds an optimal homography matrix using 4 or more point pairs (as opposed to GetPerspectiveTransform, which uses exactly 4)
drawKeyPoints(Mat src, List<KeyPoint> keypoints, Mat dst, Scalar color, DrawMatchesFlag flag) → void
drawMatches(InputArray img1, List<KeyPoint> keypoints1, InputArray img2, List<KeyPoint> keypoints2, List<DMatch> matches1to2, InputOutputArray outImg, {Scalar? matchColor, Scalar? singlePointColor, List<int>? matchesMask, DrawMatchesFlag flags = DrawMatchesFlag.DEFAULT}) → void
edgePreservingFilter(InputArray src, {int flags = 1, double sigma_s = 60, double sigma_r = 0.4}) → Mat
EdgePreservingFilter filtering is the fundamental operation in image and video processing. Edge-preserving smoothing filters are used in many different applications. For further details, please see: https://docs.opencv.org/4.x/df/dac/group__photo__render.html#gafaee2977597029bc8e35da6e67bd31f7
eigen(InputArray src, OutputArray eigenvalues, {OutputArray? eigenvectors}) → bool
Eigen calculates eigenvalues and eigenvectors of a symmetric matrix.
eigenNonSymmetric(InputArray src, OutputArray eigenvalues, OutputArray eigenvectors) → void
EigenNonSymmetric calculates eigenvalues and eigenvectors of a non-symmetric matrix (real eigenvalues only).
ellipse(InputOutputArray img, Point center, Point axes, double angle, double startAngle, double endAngle, Scalar color, {int thickness = 1, int lineType = LINE_8, int shift = 0}) → void
Ellipse draws a simple or thick elliptic arc or fills an ellipse sector.
equalizeHist(Mat src, Mat dst) → void
EqualizeHist Equalizes the histogram of a grayscale image.
erode(Mat src, Mat dst, Mat kernel, {({int x, int y}) anchor = (x: -1, y: -1), int iterations = 1, int borderType = BORDER_CONSTANT, Scalar? borderValue}) → void
Erode erodes an image by using a specific structuring element.
estimateAffine2D(List<Point2f> from, List<Point2f> to, {int method = RANSAC, double ransacReprojThreshold = 3, int maxIters = 2000, double confidence = 0.99, int refineIters = 10, OutputArray? inliers}) → (Mat, Mat)
estimateAffinePartial2D(List<Point2f> from, List<Point2f> to, {int method = RANSAC, double ransacReprojThreshold = 3, int maxIters = 2000, double confidence = 0.99, int refineIters = 10, OutputArray? inliers}) → (Mat, Mat)
exp(InputArray src, OutputArray dst) → void
Exp calculates the exponent of every array element.
extractChannel(InputArray src, OutputArray dst, int coi) → void
ExtractChannel extracts a single channel from src (coi is 0-based index).
fastNlMeansDenoising(InputArray src, {double h = 3, int templateWindowSize = 7, int searchWindowSize = 21}) → Mat
FastNlMeansDenoising performs image denoising using Non-local Means Denoising algorithm http://www.ipol.im/pub/algo/bcm_non_local_means_denoising/ For further details, please see: https://docs.opencv.org/4.x/d1/d79/group__photo__denoise.html#ga4c6b0031f56ea3f98f768881279ffe93
fastNlMeansDenoisingColored(InputArray src, {double h = 3, double hColor = 3, int templateWindowSize = 7, int searchWindowSize = 21}) → Mat
FastNlMeansDenoisingColored is a modification of fastNlMeansDenoising function for colored images. For further details, please see: https://docs.opencv.org/4.x/d1/d79/group__photo__denoise.html#ga21abc1c8b0e15f78cd3eff672cb6c476
fastNlMeansDenoisingColoredMulti(List<Mat> srcImgs, int imgToDenoiseIndex, int temporalWindowSize, {double h = 3, double hColor = 3, int templateWindowSize = 7, int searchWindowSize = 21}) → Mat
FastNlMeansDenoisingColoredMulti denoises the selected images. For further details, please see: https://docs.opencv.org/master/d1/d79/group__photo__denoise.html#gaa501e71f52fb2dc17ff8ca5e7d2d3619
fillPoly(InputOutputArray img, List<List<Point>> pts, Scalar color, {int lineType = LINE_8, int shift = 0, Point? offset}) → void
FillPolyWithParams fills the area bounded by one or more polygons.
filter2D(InputArray src, OutputArray dst, int ddepth, InputArray kernel, {Point? anchor, double delta = 0, int borderType = BORDER_DEFAULT}) → void
Filter2D applies an arbitrary linear filter to an image.
findChessboardCorners(InputArray image, Size patternSize, {OutputArray? corners, int flags = CALIB_CB_ADAPTIVE_THRESH + CALIB_CB_NORMALIZE_IMAGE}) → (bool, Mat)
findChessboardCornersSB(InputArray image, Size patternSize, int flags, {OutputArray? corners, OutputArray? meta}) → (bool, Mat, Mat)
findContours(Mat src, int mode, int method) → (Contours, Mat)
FindContours finds contours in a binary image.
findNonZero(InputArray src, OutputArray idx) → void
FindNonZero returns the list of locations of non-zero pixels.
findTransformECC(InputArray templateImage, InputArray inputImage, InputOutputArray warpMatrix, int motionType, TermCriteria criteria, InputArray inputMask, int gaussFiltSize) → double
FindTransformECC finds the geometric transform (warp) between two images in terms of the ECC criterion.
fitEllipse(List<Point> pts) → RotatedRect
FitEllipse Fits an ellipse around a set of 2D points.
fitLine(List<Point> points, OutputArray line, int distType, double param, double reps, double aeps) → void
FitLine fits a line to a 2D or 3D point set. distType: DistanceTypes For further details, please see: https:///docs.opencv.org/master/d3/dc0/group__imgproc__shape.html#gaf849da1fdafa67ee84b1e9a23b93f91f
flip(InputArray src, OutputArray dst, int flipCode) → void
Flip flips a 2D array around horizontal(0), vertical(1), or both axes(-1).
gaussianBlur(Mat src, Mat dst, Size ksize, double sigmaX, {double sigmaY = 0, int borderType = BORDER_DEFAULT}) → void
GaussianBlur blurs an image Mat using a Gaussian filter. The function convolves the src Mat image into the dst Mat using the specified Gaussian kernel params.
gemm(InputArray src1, InputArray src2, double alpha, InputArray src3, double beta, OutputArray dst, {int flags = 0}) → void
Gemm performs generalized matrix multiplication.
getAffineTransform(List<Point> src, List<Point> dst) → Mat
GetAffineTransform returns a 2x3 affine transformation matrix for the corresponding 3 point pairs as image.Point.
getAffineTransform2f(List<Point2f> src, List<Point2f> dst) → Mat
getBlobChannel(Mat blob, int imgidx, int chnidx) → Mat
GetBlobChannel extracts a single (2d)channel from a 4 dimensional blob structure (this might e.g. contain the results of a SSD or YOLO detection,
getBlobSize(Mat blob) → Scalar
GetBlobSize retrieves the 4 dimensional size information in (N,C,H,W) order
getBuildInformation() → String
Returns full configuration time cmake output.
getGaussianKernel(int ksize, double sigma, {int ktype = 6}) → Mat
GetGaussianKernel returns Gaussian filter coefficients.
getNumThreads() → int
Get the number of threads for OpenCV.
getOptimalDFTSize(int vecsize) → int
GetOptimalDFTSize returns the optimal Discrete Fourier Transform (DFT) size for a given vector size.
getOptimalNewCameraMatrix(InputArray cameraMatrix, InputArray distCoeffs, Size imageSize, double alpha, {Size newImgSize = (0, 0), bool centerPrincipalPoint = false}) → (Mat, Rect)
GetOptimalNewCameraMatrixWithParams computes and returns the optimal new camera matrix based on the free scaling parameter.
getPerspectiveTransform(List<Point> src, List<Point> dst, {int solveMethod = DECOMP_LU}) → Mat
GetPerspectiveTransform returns 3x3 perspective transformation for the corresponding 4 point pairs as image.Point.
getPerspectiveTransform2f(List<Point2f> src, List<Point2f> dst, {int solveMethod = DECOMP_LU}) → Mat
GetPerspectiveTransform2f returns 3x3 perspective transformation for the corresponding 4 point pairs as gocv.Point2f.
getRectSubPix(InputArray image, Size patchSize, Point2f center, OutputArray patch, {int patchType = -1}) → void
GetRectSubPix retrieves a pixel rectangle from an image with sub-pixel accuracy.
getRotationMatrix2D(Point2f center, double angle, double scale) → Mat
GetRotationMatrix2D calculates an affine matrix of 2D rotation.
getStructuringElement(int shape, Size ksize, {Point? anchor}) → Mat
GetStructuringElement returns a structuring element of the specified size and shape for morphological operations.
getTextSize(String text, int fontFace, double fontScale, int thickness) → (Size, int)
GetTextSizeWithBaseline calculates the width and height of a text string including the basline of the text. It returns an image.Point with the size required to draw text using a specific font face, scale, and thickness as well as its baseline.
getTickCount() → int
GetTickCount returns the number of ticks.
getTickFrequency() → double
GetTickFrequency returns the number of ticks per second.
goodFeaturesToTrack(InputArray image, OutputArray corners, int maxCorners, double qualityLevel, double minDistance, {InputArray? mask, int blockSize = 3, bool useHarrisDetector = false, double k = 0.04}) → void
GoodFeaturesToTrack determines strong corners on an image. The function finds the most prominent corners in the image or in the specified image region.
grabCut(InputArray img, InputOutputArray mask, Rect rect, InputOutputArray bgdModel, InputOutputArray fgdModel, int iterCount, {int mode = GC_EVAL}) → void
Grabcut runs the GrabCut algorithm. The function implements the GrabCut image segmentation algorithm. For further details, please see: https:///docs.opencv.org/master/d3/d47/group__imgproc__segmentation.html#ga909c1dda50efcbeaa3ce126be862b37f
groupRectangles(List<Rect> rects, int groupThreshold, double eps) → List<Rect>
hconcat(InputArray src1, InputArray src2, OutputArray dst) → void
Hconcat applies horizontal concatenation to given matrices.
HoughCircles(InputArray image, OutputArray circles, int method, double dp, double minDist, {double param1 = 100, double param2 = 100, int minRadius = 0, int maxRadius = 0}) → void
HoughCircles finds circles in a grayscale image using the Hough transform. The only "method" currently supported is HoughGradient. If you want to pass more parameters, please see HoughCirclesWithParams.
HoughLines(InputArray image, OutputArray lines, double rho, double theta, int threshold, {double srn = 0, double stn = 0, double min_theta = 0, double max_theta = CV_PI}) → void
HoughLines implements the standard or standard multi-scale Hough transform algorithm for line detection. For a good explanation of Hough transform, see: http:///homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm
HoughLinesP(InputArray image, OutputArray lines, double rho, double theta, int threshold, {double minLineLength = 0, double maxLineGap = 0}) → void
HoughLinesP implements the probabilistic Hough transform algorithm for line detection. For a good explanation of Hough transform, see: http:///homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm
HoughLinesPointSet(InputArray point, OutputArray lines, int lines_max, int threshold, double min_rho, double max_rho, double rho_step, double min_theta, double max_theta, double theta_step) → void
HoughLinesPointSet implements the Hough transform algorithm for line detection on a set of points. For a good explanation of Hough transform, see: http:///homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm
idct(InputArray src, OutputArray dst, {int flags = 0}) → void
IDCT calculates the inverse Discrete Cosine Transform of a 1D or 2D array.
idft(InputArray src, OutputArray dst, {int flags = 0, int nonzeroRows = 0}) → void
IDFT calculates the inverse Discrete Fourier Transform of a 1D or 2D array.
illuminationChange(InputArray src, InputArray mask, {double alpha = 0.2, double beta = 0.4}) → Mat
IlluminationChange modifies locally the apparent illumination of an image. For further details, please see: https://docs.opencv.org/master/df/da0/group__photo__clone.html#gac5025767cf2febd8029d474278e886c7
imagesFromBlob(Mat blob) → List<Mat>
ImagesFromBlob Parse a 4D blob and output the images it contains as 2D arrays through a simpler data structure (std::vectorcv::Mat).
imdecode(Uint8List buf, int flags, {Mat? dst = null}) → Mat
imdecode reads an image from a buffer in memory. The function imdecode reads an image from the specified buffer in memory. If the buffer is too short or contains invalid data, the function returns an empty matrix. @param buf Input array or vector of bytes. @param flags The same flags as in cv::imread, see cv::ImreadModes. For further details, please see: https://docs.opencv.org/master/d4/da8/group__imgcodecs.html#ga26a67788faa58ade337f8d28ba0eb19e
imencode(String ext, InputArray img, {List<int>? params}) → Uint8List
IMEncode encodes an image Mat into a memory buffer. This function compresses the image and stores it in the returned memory buffer, using the image format passed in in the form of a file extension string.
imread(String filename, {int flags = IMREAD_COLOR}) → Mat
IMRead reads an image from a file into a Mat. The flags param is one of the IMReadFlag flags. If the image cannot be read (because of missing file, improper permissions, unsupported or invalid format), the function returns an empty Mat.
imwrite(String filename, InputArray img, {List<int>? params}) → bool
IMWrite writes a Mat to an image file.
initUndistortRectifyMap(InputArray cameraMatrix, InputArray distCoeffs, InputArray R, InputArray newCameraMatrix, Size size, int m1type, {OutputArray? map1, OutputArray? map2}) → (Mat, Mat)
InitUndistortRectifyMap computes the joint undistortion and rectification transformation and represents the result in the form of maps for remap
inpaint(InputArray src, InputArray inpaintMask, double inpaintRadius, int flags) → Mat
Inpaint 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. For further details, please see: https://docs.opencv.org/4.x/d7/d8b/group__photo__inpaint.html#gaedd30dfa0214fec4c88138b51d678085
inRange(InputArray src, InputArray lowerb, InputArray upperb, OutputArray dst) → void
InRange checks if array elements lie between the elements of two Mat arrays.
inRangebyScalar(InputArray src, Scalar lowerb, Scalar upperb, OutputArray dst) → void
InRangeWithScalar checks if array elements lie between the elements of two Scalars
insertChannel(InputArray src, InputOutputArray dst, int coi) → void
InsertChannel inserts a single channel to dst (coi is 0-based index) (it replaces channel i with another in dst).
integral(InputArray src, OutputArray sum, OutputArray sqsum, OutputArray tilted, {int sdepth = -1, int sqdepth = -1}) → void
Integral calculates one or more integral images for the source image. For further details, please see: https:///docs.opencv.org/master/d7/d1b/group__imgproc__misc.html#ga97b87bec26908237e8ba0f6e96d23e28
invert(InputArray src, OutputArray dst, {int flags = DECOMP_LU}) → double
Invert finds the inverse or pseudo-inverse of a matrix.
invertAffineTransform(InputArray M, {OutputArray? iM}) → Mat
Inverts an affine transformation. The function computes an inverse affine transformation represented by 2×3 matrix M: The result is also a 2×3 matrix of the same type as M.
kmeans(InputArray data, int K, InputOutputArray bestLabels, TermCriteria criteria, int attempts, int flags, OutputArray centers) → double
KMeans finds centers of clusters and groups input samples around the clusters.
kmeansByPoints(List<Point> data, int K, InputOutputArray bestLabels, TermCriteria criteria, int attempts, int flags, OutputArray centers) → double
KMeansPoints finds centers of clusters and groups input samples around the clusters.
Laplacian(Mat src, Mat dst, int ddepth, {int ksize = 1, double scale = 1, double delta = 0, int borderType = BORDER_DEFAULT}) → void
Laplacian calculates the Laplacian of an image.
line(InputOutputArray img, Point pt1, Point pt2, Scalar color, {int thickness = 1, int lineType = LINE_8, int shift = 0}) → void
Line draws a line segment connecting two points.
linearPolar(InputArray src, OutputArray dst, Point2f center, double maxRadius, int flags) → void
LinearPolar remaps an image to polar coordinates space.
loadNativeLibrary() → DynamicLibrary
log(InputArray src, OutputArray dst) → void
Log calculates the natural logarithm of every array element.
logPolar(InputArray src, OutputArray dst, Point2f center, double M, int flags) → void
LogPolar remaps an image to semilog-polar coordinates space.
magnitude(InputArray x, InputArray y, OutputArray magnitude) → void
Magnitude calculates the magnitude of 2D vectors.
matchShapes(List<Point> contour1, List<Point> contour2, int method, double parameter) → double
Compares two shapes. method: ShapeMatchModes For further details, please see: https:///docs.opencv.org/4.x/d3/dc0/group__imgproc__shape.html#gaadc90cb16e2362c9bd6e7363e6e4c317
matchTemplate(Mat image, Mat templ, Mat result, int method, {Mat? mask}) → void
MatchTemplate compares a template against overlapped image regions.
max(InputArray src1, InputArray src2, OutputArray dst) → void
Max calculates per-element maximum of two arrays or an array and a scalar.
meanStdDev(InputArray src, OutputArray mean, OutputArray stddev, {InputArray? mask}) → dynamic
MeanStdDev calculates a mean and standard deviation of array elements.
medianBlur(Mat src, OutputArray dst, int ksize) → void
MedianBlur blurs an image using the median filter.
merge(List<Mat> mv, OutputArray dst) → void
Merge creates one multi-channel array out of several single-channel ones.
min(InputArray src1, InputArray src2, OutputArray dst) → void
Min calculates per-element minimum of two arrays or an array and a scalar.
minAreaRect(List<Point> points) → RotatedRect
MinAreaRect finds a rotated rectangle of the minimum area enclosing the input 2D point set.
minEnclosingCircle(List<Point> pts) → (Point2f, double)
MinEnclosingCircle finds a circle of the minimum area enclosing the input 2D point set.
minMaxIdx(InputArray src, {InputArray? mask}) → (double, double, int, int)
MinMaxIdx finds the global minimum and maximum in an array.
minMaxLoc(InputArray src, {InputArray? mask}) → (double, double, Point, Point)
MinMaxLoc finds the global minimum and maximum in an array.
moments(Mat src, {bool binaryImage = false}) → Moments
Moments calculates all of the moments up to the third order of a polygon or rasterized shape.
morphologyDefaultBorderValue() → Scalar
MorphologyDefaultBorder returns "magic" border value for erosion and dilation. It is automatically transformed to Scalar::all(-DBL_MAX) for dilation.
morphologyEx(Mat src, Mat dst, int op, Mat kernel, {Point? anchor, int iterations = 1, int borderType = BORDER_CONSTANT}) → void
MorphologyEx performs advanced morphological transformations.
mulSpectrums(InputArray a, InputArray b, OutputArray c, int flags, {bool conjB = false}) → void
Copies specified channels from input arrays to the specified channels of output arrays.
multiply(InputArray src1, InputArray src2, OutputArray dst, {double scale = 1, int dtype = -1}) → void
Multiply calculates the per-element scaled product of two arrays. Both input arrays must be of the same size and the same type.
NMSBoxes(List<Rect> bboxes, List<double> scores, double score_threshold, double nms_threshold, {double eta = 1.0, int top_k = 0}) → List<int>
NMSBoxes performs non maximum suppression given boxes and corresponding scores.
norm(InputArray src1, {int normType = NORM_L2, InputArray? mask}) → double
Norm calculates the absolute norm of an array.
norm1(InputArray src1, InputArray src2, {int normType = NORM_L2, InputArray? mask}) → double
Norm calculates the absolute difference/relative norm of two arrays.
normalize(InputArray src, InputOutputArray dst, {double alpha = 1, double beta = 0, int norm_type = NORM_L2}) → void
Normalize normalizes the norm or value range of an array.
openCvVersion() → String
get version
PCACompute(InputArray data, InputOutputArray mean, OutputArray eigenvectors, OutputArray eigenvalues, {int maxComponents = 0}) → void
PCACompute performs PCA.
pencilSketch(InputArray src, {double sigma_s = 60, double sigma_r = 0.07, double shade_factor = 0.02}) → (Mat, Mat)
PencilSketch pencil-like non-photorealistic line drawing. For further details, please see: https://docs.opencv.org/4.x/df/dac/group__photo__render.html#gae5930dd822c713b36f8529b21ddebd0c
perspectiveTransform(InputArray src, OutputArray dst, InputArray m) → void
PerspectiveTransform performs the perspective matrix transformation of vectors.
phase(InputArray x, InputArray y, OutputArray angle, {bool angleInDegrees = false}) → void
Phase calculates the rotation angle of 2D vectors.
pointPolygonTest(List<Point> pts, Point2f pt, bool measureDist) → double
PointPolygonTest performs a point-in-contour test.
polarToCart(InputArray magnitude, InputArray angle, OutputArray x, OutputArray y, {bool angleInDegrees = false}) → void
PolatToCart calculates x and y coordinates of 2D vectors from their magnitude and angle.
polylines(InputOutputArray img, List<List<Point>> pts, bool isClosed, Scalar color, {int thickness = 1, int lineType = LINE_8, int shift = 0}) → void
Polylines draws several polygonal curves.
pow(InputArray src, double power, OutputArray dst) → void
Pow raises every array element to a power.
putText(InputOutputArray img, String text, Point org, int fontFace, double fontScale, Scalar color, {int thickness = 1, int lineType = LINE_8, bool bottomLeftOrigin = false}) → void
PutTextWithParams draws a text string. It renders the specified text string into the img Mat at the location passed in the "org" param, using the desired font face, font scale, color, and line thinkness.
pyrDown(Mat src, Mat dst, {(int, int) dstsize = (0, 0), int borderType = BORDER_CONSTANT}) → void
PyrDown blurs an image and downsamples it.
pyrUp(Mat src, Mat dst, {(int, int) dstsize = (0, 0), int borderType = BORDER_CONSTANT}) → void
PyrUp upsamples an image and then blurs it.
randn(InputOutputArray dst, Scalar mean, Scalar stddev) → void
RandN Fills the array with normally distributed random numbers.
randShuffle(InputOutputArray dst, {double iterFactor = 1, Rng? rng}) → void
RandShuffle Shuffles the array elements randomly.
randu(InputOutputArray dst, Scalar low, Scalar high) → void
RandU Generates a single uniformly-distributed random number or an array of random numbers.
rectangle(InputOutputArray img, Rect rect, Scalar color, {int thickness = 1, int lineType = LINE_8, int shift = 0}) → void
Rectangle draws a simple, thick, or filled up-right rectangle. It renders a rectangle with the desired characteristics to the target Mat image.
reduce(InputArray src, OutputArray dst, int dim, int rtype, {int dtype = -1}) → void
Reduce reduces a matrix to a vector.
reduceArgMax(InputArray src, OutputArray dst, int axis, {bool lastIndex = false}) → void
Finds indices of max elements along provided axis.
reduceArgMin(InputArray src, OutputArray dst, int axis, {bool lastIndex = false}) → void
Finds indices of min elements along provided axis.
registerErrorCallback({DartErrorCallbackFunction? callback}) → void
remap(InputArray src, OutputArray dst, InputArray map1, InputArray map2, int interpolation, {int borderMode = BORDER_CONSTANT, Scalar? borderValue}) → void
Remap applies a generic geometrical transformation to an image.
repeat(InputArray src, int ny, int nx, OutputArray dst) → void
Repeat fills the output array with repeated copies of the input array.
resize(InputArray src, OutputArray dst, Size dsize, {double fx = 0, double fy = 0, int interpolation = INTER_LINEAR}) → void
Resize resizes an image. It resizes the image src down to or up to the specified size, storing the result in dst. Note that src and dst may be the same image. If you wish to scale by factor, an empty sz may be passed and non-zero fx and fy. Likewise, if you wish to scale to an explicit size, a non-empty sz may be passed with zero for both fx and fy.
rotate(InputArray src, OutputArray dst, int rotateCode) → void
Rotate rotates a 2D array in multiples of 90 degrees
scaleAdd(InputArray src1, double alpha, InputArray src2, OutputArray dst) → void
Calculates the sum of a scaled array and another array.
Scharr(Mat src, Mat dst, int ddepth, int dx, int dy, {double scale = 1, double delta = 0, int borderType = BORDER_DEFAULT}) → void
Scharr calculates the first x- or y- image derivative using Scharr operator.
seamlessClone(InputArray src, InputArray dst, InputArray mask, Point p, int flags) → Mat
SeamlessClone blend two image by Poisson Blending. For further details, please see: https://docs.opencv.org/master/df/da0/group__photo__clone.html#ga2bf426e4c93a6b1f21705513dfeca49d
sepFilter2D(InputArray src, OutputArray dst, int ddepth, InputArray kernelX, InputArray kernelY, {Point? anchor, double delta = 0, int borderType = BORDER_DEFAULT}) → void
SepFilter2D applies a separable linear filter to the image.
setIdentity(InputOutputArray mtx, {double s = 1}) → void
SetIdentity initializes a scaled identity matrix. For further details, please see:
setNumThreads(int n) → void
Set the number of threads for OpenCV.
sobel(Mat src, Mat dst, int ddepth, int dx, int dy, {int ksize = 3, double scale = 1, double delta = 0, int borderType = BORDER_DEFAULT}) → void
Sobel calculates the first, second, third, or mixed image derivatives using an extended Sobel operator
solve(InputArray src1, InputArray src2, OutputArray dst, {int flags = DECOMP_LU}) → bool
Solve solves one or more linear systems or least-squares problems.
solveCubic(InputArray coeffs, OutputArray roots) → int
SolveCubic finds the real roots of a cubic equation.
solvePoly(InputArray coeffs, OutputArray roots, {int maxIters = 300}) → double
SolvePoly finds the real or complex roots of a polynomial equation.
sort(InputArray src, OutputArray dst, int flags) → void
Sort sorts each row or each column of a matrix.
sortIdx(InputArray src, OutputArray dst, int flags) → void
SortIdx sorts each row or each column of a matrix. Instead of reordering the elements themselves, it stores the indices of sorted elements in the output array
spatialGradient(Mat src, Mat dx, Mat dy, {int ksize = 3, int borderType = BORDER_DEFAULT}) → void
SpatialGradient calculates the first order image derivative in both x and y using a Sobel operator.
split(InputArray m) → List<Mat>
Split creates an array of single channel images from a multi-channel image Created images should be closed manualy to avoid memory leaks.
sqrBoxFilter(Mat src, Mat dst, int depth, Size ksize) → void
SqBoxFilter calculates the normalized sum of squares of the pixel values overlapping the filter.
stylization(InputArray src, {double sigma_s = 60, double sigma_r = 0.45}) → Mat
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. For further details, please see: https://docs.opencv.org/4.x/df/dac/group__photo__render.html#gacb0f7324017df153d7b5d095aed53206
subtract(InputArray src1, InputArray src2, OutputArray dst) → void
Subtract calculates the per-element subtraction of two arrays or an array and a scalar.
termCriteriaNew(int type, int maxCount, double epsilon) → TermCriteria
TermCriteria is the criteria for iterative algorithms.
textureFlattening(InputArray src, InputArray mask, {double low_threshold = 30, double high_threshold = 45, int kernel_size = 3}) → Mat
TextureFlattening washes out the texture of the selected region, giving its contents a flat aspect. For further details, please see: https://docs.opencv.org/master/df/da0/group__photo__clone.html#gad55df6aa53797365fa7cc23959a54004
theRNG() → Rng
TheRNG Returns the default random number generator.
threshold(InputArray src, OutputArray dst, double thresh, double maxval, int type) → double
Threshold applies a fixed-level threshold to each array element.
throwIfFailed(CvStatus status) → void
trace(InputArray mtx) → Scalar
Trace returns the trace of a matrix.
transform(InputArray src, OutputArray dst, InputArray m) → void
Transform performs the matrix transformation of every array element.
transpose(InputArray src, OutputArray dst) → void
Transpose transposes a matrix.
undistort(InputArray src, InputArray cameraMatrix, InputArray distCoeffs, {OutputArray? dst, InputArray? newCameraMatrix}) → Mat
undistortPoints(InputArray src, InputArray cameraMatrix, InputArray distCoeffs, {OutputArray? dst, InputArray? R, InputArray? P}) → Mat
vconcat(InputArray src1, InputArray src2, OutputArray dst) → void
Vconcat applies vertical concatenation to given matrices.
waitKey(int delay) → int
WaitKey that is not attached to a specific Window. Only use when no Window exists in your application, e.g. command line app.
warpAffine(InputArray src, OutputArray dst, InputArray M, Size dsize, {int flags = INTER_LINEAR, int borderMode = BORDER_CONSTANT, Scalar? borderValue}) → void
WarpAffine applies an affine transformation to an image.
warpPerspective(InputArray src, OutputArray dst, InputArray M, Size dsize, {int flags = INTER_LINEAR, int borderMode = BORDER_CONSTANT, Scalar? borderValue}) → void
WarpPerspective applies a perspective transformation to an image. For more parameters please check WarpPerspectiveWithParams.
watershed(InputArray image, InputOutputArray markers) → void
Watershed performs a marker-based image segmentation using the watershed algorithm.

Typedefs

InputArray = OutputArray
InputOutputArray = Mat
OutputArray = Mat
Size = (int, int)
(width, height)

Exceptions / Errors

OpenCvDartException
OpenCvException