OpenCV  3.3.1
Open Source Computer Vision
Modules | Classes | Enumerations | Functions
Image processing


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


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


enum  cv::HoughModes {
 Variants of a Hough transform. More...


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

Detailed Description

Enumeration Type Documentation

§ HoughModes

Variants of a Hough transform.


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


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.


multi-scale variant of the classical Hough transform. The lines are encoded the same way as HOUGH_STANDARD.


basically 21HT, described in [200]

Function Documentation

§ blendLinear()

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

Performs linear blending of two images:

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

src1It has a type of CV_8UC(n) or CV_32FC(n), where n is a positive integer.
src2It has the same type and size as src1.
weights1It has a type of CV_32FC1 and the same size with src1.
weights2It has a type of CV_32FC1 and the same size with src1.
dstIt 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) 

§ 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