Image processing

Modules
	Image Filtering
	Geometric Image Transformations
	Miscellaneous Image Transformations
	Drawing Functions
	ColorMaps in OpenCV
	Planar Subdivision
	Histograms
	Structural Analysis and Shape Descriptors
	Motion Analysis and Object Tracking
	Feature Detection
	Object Detection
	C API
	Hardware Acceleration Layer

Classes
class	cv::CLAHE
class	cv::GeneralizedHough
	finds arbitrary template in the grayscale image using Generalized Hough Transform More...
class	cv::GeneralizedHoughBallard
class	cv::GeneralizedHoughGuil

Enumerations
enum	cv::HoughModes {   cv::HOUGH_STANDARD = 0,   cv::HOUGH_PROBABILISTIC = 1,   cv::HOUGH_MULTI_SCALE = 2,   cv::HOUGH_GRADIENT = 3 }
	Variants of a Hough transform. More...

Functions
void	cv::blendLinear (InputArray src1, InputArray src2, InputArray weights1, InputArray weights2, OutputArray dst)
Ptr< CLAHE >	cv::createCLAHE (double clipLimit=40.0, Size tileGridSize=Size(8, 8))
Ptr< GeneralizedHoughBallard >	cv::createGeneralizedHoughBallard ()
Ptr< GeneralizedHoughGuil >	cv::createGeneralizedHoughGuil ()
void	cv::demosaicing (InputArray _src, OutputArray _dst, int code, int dcn=0)

Detailed Description

Enumeration Type Documentation

HoughModes

enum cv::HoughModes

Variants of a Hough transform.

Enumerator
HOUGH_STANDARD  Python: cv.HOUGH_STANDARD	classical or standard Hough transform. Every line is represented by two floating-point numbers \((\rho, \theta)\) , where \(\rho\) is a distance between (0,0) point and the line, and \(\theta\) is the angle between x-axis and the normal to the line. Thus, the matrix must be (the created sequence will be) of CV_32FC2 type
HOUGH_PROBABILISTIC  Python: cv.HOUGH_PROBABILISTIC	probabilistic Hough transform (more efficient in case if the picture contains a few long linear segments). It returns line segments rather than the whole line. Each segment is represented by starting and ending points, and the matrix must be (the created sequence will be) of the CV_32SC4 type.
HOUGH_MULTI_SCALE  Python: cv.HOUGH_MULTI_SCALE	multi-scale variant of the classical Hough transform. The lines are encoded the same way as HOUGH_STANDARD.
HOUGH_GRADIENT  Python: cv.HOUGH_GRADIENT	basically 21HT, described in [208]

Function Documentation

blendLinear()

void cv::blendLinear	(	InputArray	src1,
		InputArray	src2,
		InputArray	weights1,
		InputArray	weights2,
		OutputArray	dst
	)

Performs linear blending of two images:

\[ \texttt{dst}(i,j) = \texttt{weights1}(i,j)*\texttt{src1}(i,j) + \texttt{weights2}(i,j)*\texttt{src2}(i,j) \]

Parameters

src1	It has a type of CV_8UC(n) or CV_32FC(n), where n is a positive integer.
src2	It has the same type and size as src1.
weights1	It has a type of CV_32FC1 and the same size with src1.
weights2	It has a type of CV_32FC1 and the same size with src1.
dst	It is created if it does not have the same size and type with src1.

createCLAHE()

Ptr<CLAHE> cv::createCLAHE	(	double	clipLimit = 40.0,
		Size	tileGridSize = Size(8, 8)
	)

Python:
	retval	=	cv.createCLAHE(	[, clipLimit[, tileGridSize]]	)

createGeneralizedHoughBallard()

Ptr<GeneralizedHoughBallard> cv::createGeneralizedHoughBallard

(

)

Ballard, D.H. (1981). Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognition 13 (2): 111-122. Detects position only without translation and rotation

createGeneralizedHoughGuil()

Ptr<GeneralizedHoughGuil> cv::createGeneralizedHoughGuil

(

)

Guil, N., González-Linares, J.M. and Zapata, E.L. (1999). Bidimensional shape detection using an invariant approach. Pattern Recognition 32 (6): 1025-1038. Detects position, translation and rotation

demosaicing()

void cv::demosaicing	(	InputArray	_src,
		OutputArray	_dst,
		int	code,
		int	dcn = 0
	)

Python:
	_dst	=	cv.demosaicing(	_src, code[, _dst[, dcn]]	)