OpenCV
3.1.0-dev
Open Source Computer Vision
|
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) |
Performs linear blending of two images. More... | |
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) |
enum cv::HoughModes |
Variants of a Hough transform.
Enumerator | |
---|---|
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 |
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 |
multi-scale variant of the classical Hough transform. The lines are encoded the same way as HOUGH_STANDARD. |
HOUGH_GRADIENT |
basically 21HT, described in [166] |
void cv::blendLinear | ( | InputArray | src1, |
InputArray | src2, | ||
InputArray | weights1, | ||
InputArray | weights2, | ||
OutputArray | dst | ||
) |
Performs linear blending of two images.
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 traslation and rotation
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, traslation and rotation
void cv::demosaicing | ( | InputArray | _src, |
OutputArray | _dst, | ||
int | code, | ||
int | dcn = 0 |
||
) |